Letteratura scientifica selezionata sul tema "Domain translation"
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Articoli di riviste sul tema "Domain translation"
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Li, Rumeng, Xun Wang e Hong Yu. "MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation". Proceedings of the AAAI Conference on Artificial Intelligence 34, n. 05 (3 aprile 2020): 8245–52. http://dx.doi.org/10.1609/aaai.v34i05.6339.
Testo completoAbstract (sommario):
Neural machine translation (NMT) models have achieved state-of-the-art translation quality with a large quantity of parallel corpora available. However, their performance suffers significantly when it comes to domain-specific translations, in which training data are usually scarce. In this paper, we present a novel NMT model with a new word embedding transition technique for fast domain adaption. We propose to split parameters in the model into two groups: model parameters and meta parameters. The former are used to model the translation while the latter are used to adjust the representational space to generalize the model to different domains. We mimic the domain adaptation of the machine translation model to low-resource domains using multiple translation tasks on different domains. A new training strategy based on meta-learning is developed along with the proposed model to update the model parameters and meta parameters alternately. Experiments on datasets of different domains showed substantial improvements of NMT performances on a limited amount of data.
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Djebaili, Baya. "ترجمة النص المالي". Traduction et Langues 14, n. 1 (31 agosto 2015): 243–54. http://dx.doi.org/10.52919/translang.v14i1.787.
Testo completoAbstract (sommario):
Financial Text Translation
Nowadays, specialized translation has acquired a great importance in different fields, particularly in the domain of affairs and finance. The domains covered by financial translation are various. They range from simple economic research to the most complex accounting studies. It is thus necessary for this translation to have a deep knowledge of the issue to be tackled, and a total mastery of the mechanisms of finance. Translating the financial terminology is the greatest obstacle that a translator meets in his work. This is due to its technical nature as well as the different neologisms regularly used in the language of finance. The most important element of this piece of work is the importance of research in both documentation and terminology. If these two elements are properly carried out, they allow the translator to translate any specialized text in any field without necessarily being a specialist in the domain in question. Nevertheless, he must usually carry out continuous research and have a universal knowledge. He also has to enrich and update his databases and terminological files to make of his translation a driving force for the coming translations.
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Marie, Benjamin, e Atsushi Fujita. "Synthesizing Parallel Data of User-Generated Texts with Zero-Shot Neural Machine Translation". Transactions of the Association for Computational Linguistics 8 (novembre 2020): 710–25. http://dx.doi.org/10.1162/tacl_a_00341.
Testo completoAbstract (sommario):
Neural machine translation (NMT) systems are usually trained on clean parallel data. They can perform very well for translating clean in-domain texts. However, as demonstrated by previous work, the translation quality significantly worsens when translating noisy texts, such as user-generated texts (UGT) from online social media. Given the lack of parallel data of UGT that can be used to train or adapt NMT systems, we synthesize parallel data of UGT, exploiting monolingual data of UGT through crosslingual language model pre-training and zero-shot NMT systems. This paper presents two different but complementary approaches: One alters given clean parallel data into UGT-like parallel data whereas the other generates translations from monolingual data of UGT. On the MTNT translation tasks, we show that our synthesized parallel data can lead to better NMT systems for UGT while making them more robust in translating texts from various domains and styles.
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Xiang, Cailing. "Study on the Effectiveness of ChatGPT in Translating Forestry Sci-tech Texts". International Journal of Linguistics, Literature and Translation 7, n. 9 (29 agosto 2024): 88–94. http://dx.doi.org/10.32996/ijllt.2024.7.9.11.
Testo completoAbstract (sommario):
ChatGPT, an advanced language model by OpenAI, enhances translation with its powerful language generation and understanding. In comparison to traditional human translation, ChatGPT is less costly, time-consuming, and knowledge-constraint, showcasing the substantial value of its application in translation practice. In the context of globalization, forestry translation plays an increasing role in facilitating global forestry development. To meet the growing need for efficient and high-quality translation in the forestry sector, this paper did research on the effectiveness of ChatGPT in the translation of forestry sci-tech texts. Combining quantitative analysis using BLEU and TER scores with qualitative evaluations by domain experts, this study compares the quality of translations produced by ChatGPT with that of the three mainstream machine translation tools in the market—Google Translate, Youdao Translation, and DeepL Translator regarding the translations’ accuracy and readability. The findings reveal that while ChatGPT excels in domain-specific terminology and context-sensitive meanings, it faces challenges in dealing with texts with special sentence structures and making the translations adaptable. By identifying the strengths and limitations of ChatGPT in translating forestry sci-tech texts, this research illustrates that there is great potential for ChatGPT’s application in forestry translation. Additionally, the study provides insights that can guide the development and refinement of machine translation systems to better meet the needs of specialized fields, ultimately facilitating more effective global communication and knowledge sharing.
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Sokolova, Natalia. "Machine vs Human Translation in the Synergetic Translation Space". Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije, n. 6 (febbraio 2021): 89–98. http://dx.doi.org/10.15688/jvolsu2.2021.6.8.
Testo completoAbstract (sommario):
The paper focuses on English-to-Russian translations of patent applications on the website of the World Intellectual Property Organization (WIPO). A comparative analysis of patent applications is performed by using translations made with the help of the WIPO Translate tool and human translators within the framework of the synergetic translation space concept encompassing the domains of the author's intensions, text content and composition, energy, translator, recipient, and the translation acceptability notion. The translation erratology aspects were considered from the point of view of the semantic, referential, and syntactic ambiguity within the domains of content-composition and energy space. In the domain of the author, the intention to convey some technical information is revealed, while its rendering in the content-composition and energy domains depends on whether the translation is made by a person or a machine. Genre- and composition-related specifics have been rendered in both cases while machine translation errors have been proven to result from the semantic, referential, or syntactic ambiguity, and this is when the translated output is generally considered unacceptable by the recipient. The results obtained can be used for editing machine translations of patent documentation, assessing the quality of technical documentation translation that is referred to other specific genre conventions.
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Yin, Xu, Yan Li e Byeong-Seok Shin. "DAGAN: A Domain-Aware Method for Image-to-Image Translations". Complexity 2020 (28 marzo 2020): 1–15. http://dx.doi.org/10.1155/2020/9341907.
Testo completoAbstract (sommario):
The image-to-image translation method aims to learn inter-domain mappings from paired/unpaired data. Although this technique has been widely used for visual predication tasks—such as classification and image segmentation—and achieved great results, we still failed to perform flexible translations when attempting to learn different mappings, especially for images containing multiple instances. To tackle this problem, we propose a generative framework DAGAN (Domain-aware Generative Adversarial etwork) that enables domains to learn diverse mapping relationships. We assumed that an image is composed with background and instance domain and then fed them into different translation networks. Lastly, we integrated the translated domains into a complete image with smoothed labels to maintain realism. We examined the instance-aware framework on datasets generated by YOLO and confirmed that this is capable of generating images of equal or better diversity compared to current translation models.
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Bernaerts, Lars, Liesbeth De Bleeker e July De Wilde. "Narration and translation". Language and Literature: International Journal of Stylistics 23, n. 3 (31 luglio 2014): 203–12. http://dx.doi.org/10.1177/0963947014536504.
Testo completoAbstract (sommario):
This opening essay of the special issue on ‘Narration and Translation’ discusses the overlaps between the fields of narratology and translation studies. The fact that translation scholars have merely skimmed the surface of narratological issues relevant for the study of translation can be understood within the context of early developments in translation studies. The first explicit use of narratological models in this discipline has grown out of unease with the extant focus on the macrostructural level of translations. In recent decades, translation scholars have begun to include narrative approaches in their research. Some conceptualize the translator’s discursive presence by referring to a model of narrative communication, or borrow concepts from narratology in order to analyse observed shifts in literary translations. Outside the domain of literary translation studies, scholars have looked into the way translation can refashion narratives in the real world. Conversely, narrative theories have rarely dealt with translational issues, even though they often rely on translations of literary texts. The issue as a whole wants to enhance the dialogue between narratology and translation studies. Each essay explores aspects of the relation between narration and translation.
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Dai, Diwei. "A Study on Application of Construal Theory in English Translation of Chinese Medical book: take English Translation of Jin Gui Yao Liao as an Example". International Journal of Public Health and Medical Research 1, n. 1 (25 marzo 2024): 20–28. http://dx.doi.org/10.62051/ijphmr.v1n1.03.
Testo completoAbstract (sommario):
The translation of Chinese medical books is characterized by language, cultural connotation and cognitive domain background, which makes the translators encounter different levels of difficulty in the process of translation and form different translations accordingly. This paper takes the construal theory in cognitive linguistics as a guide, analyzes the important role of four kinds of means of construal in the process of translating Chinese medical books, and analyzes the different English translations of JGL under the construal theory, analyzes that different translations reflect different cognitive mechanisms of the translators, and comes to the conclusion that the translations should maximally reflect the cognitive points of reference of the authors at that time, so as to maximize the translations. It is also concluded that the translation should maximally reflect the author's cognitive reference point so as to maximize the closeness of the translation to the original text, aiming to provide theoretical guidance and reference for the English translation of Chinese medical books.
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Karatsiolis, Savvas, Christos N. Schizas e Nicolai Petkov. "Modular domain-to-domain translation network". Neural Computing and Applications 32, n. 11 (26 luglio 2019): 6779–91. http://dx.doi.org/10.1007/s00521-019-04358-8.
Testo completo
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Marie, Benjamin, e Atsushi Fujita. "Phrase Table Induction Using In-Domain Monolingual Data for Domain Adaptation in Statistical Machine Translation". Transactions of the Association for Computational Linguistics 5 (dicembre 2017): 487–500. http://dx.doi.org/10.1162/tacl_a_00075.
Testo completoAbstract (sommario):
We present a new framework to induce an in-domain phrase table from in-domain monolingual data that can be used to adapt a general-domain statistical machine translation system to the targeted domain. Our method first compiles sets of phrases in source and target languages separately and generates candidate phrase pairs by taking the Cartesian product of the two phrase sets. It then computes inexpensive features for each candidate phrase pair and filters them using a supervised classifier in order to induce an in-domain phrase table. We experimented on the language pair English–French, both translation directions, in two domains and obtained consistently better results than a strong baseline system that uses an in-domain bilingual lexicon. We also conducted an error analysis that showed the induced phrase tables proposed useful translations, especially for words and phrases unseen in the parallel data used to train the general-domain baseline system.
Tesi sul tema "Domain translation"
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Brunello, Marco. "Domain and genre dependency in Statistical Machine Translation". Thesis, University of Leeds, 2014. http://etheses.whiterose.ac.uk/8420/.
Testo completoAbstract (sommario):
Statistical Machine Translation (SMT) is currently the most promising and widely studied paradigm in the broader field of Machine Translation, continuously explored in order to improve its performance and to find solutions to its current shortcomings, in particular the sparsity of big bilingual corpora in a variety of domains or genres to be used as training data. However, while one the main trends is still to rely as much as possible on already available large collections of data, even when they do not fit quite well specific translation tasks in terms of relatedness of content, the possibility of using less but appropriately selected training sets - depending on the textual variety of the documents that need to be translated case by case - has not been extensively explored as much so far. The goal of this research is to investigate whether this latter possibility, i.e. the lack of availability of large quantities of assorted data, can have a possible solution in the application of strategies commonly used in genre and domain classification (including unsupervised topic modeling and document dissimilarity techniques), in particular performing subsampling experiments on bilingual corpora in order to obtain a good fit between training data and the texts that need to be translated with SMT. For the purposes of this study, already existing freely available large corpora were found to be unsuitable for the selection of domain/document specifc subsamples, so two new parallel corpora - English-Italian and English-German - were compiled employing the \web as corpus" approach on websites containing translated content. Then some tests were made on documents belonging to different varieties, translated with SMT systems built using subsamples of training data selected using document dissimilarity measures in order to pick up the most suitable documents as training data. Such method has shown how the choice of subsampling strategy heavily depends on the text variety of each considered document, but it has also proven that better translation results can be obtained from small samples of training sets rather than using all the available data, which brings benefits also in terms of quicker training times and use of fewer computational resources.
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Mayet, Tsiry. "Multi-domain translation in a semi-supervised setting". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR46.
Testo completoAbstract (sommario):
Cette thèse explore la génération multi-modale dans un contexte d'apprentissage semi-supervisé, en abordant deux défis cruciaux : la prise en charge de configurations flexibles d'entrées et de sorties à travers plusieurs domaines, et le développement d'une stratégie d'entraînement efficace des données semi-supervisées. Alors que les systèmes d'intelligence artificielle progressent, il existe un besoin croissant de modèles capables d'intégrer et de générer de manière flexible plusieurs modalités, reflétant les capacités cognitives humaines. Les systèmes d'apprentissage profond conventionnels peinent souvent lorsqu'ils s'écartent de leur configuration d'entraînement, notamment lorsque certaines modalités sont indisponibles dans les applications réelles. Par exemple, dans le domaine médical, les patients pourraient ne pas faire tous les examens possibles pour un système d'analyse complet. Obtenir un contrôle plus fin sur les modalités générées est crucial pour améliorer les capacités de génération et fournir des informations contextuelles plus riches. De plus, l'augmentation du nombre de domaines rend plus difficile l'obtention d'une supervision simultanée. Nous nous concentrons sur la translation multi-domaine dans un contexte semi-supervisé, étendant le paradigme classique de translation de domaine. Plutôt que de considérer une direction de translation spécifique ou de les limiter entre paires de domaines, nous développons des méthodes facilitant les translations entre toutes les configurations possibles de domaines. L'aspect semi-supervisé reflète des scénarios réels où une annotation complète des données est souvent infaisable ou prohibitivement coûteuse. Notre travail présente trois contributions : (1) l'étude des fonctions de régularisation pour l'espace latent avec une supervision limitée, (2) l'étude de la mise à l'échelle et de la flexibilité des modèles de translation basés sur les modèles de diffusion, et (3) l'amélioration de la vitesse de génération des modèles d'inpainting par diffusion. Premièrement, nous proposons LSM, un modèle de translation semi-supervisé exploitant des données d'entrée supplémentaires et des données de sortie structurées pour régulariser les dépendances inter-domaines et intra-domaines. Deuxièmement, nous développons MDD, un modèle semi-supervisé de translation multi-domaine basé sur la diffusion. MDD transforme la fonction de perte classique des modèles de diffusion d'une fonction de reconstruction vers une fonction de translations en modélisant différents niveaux de bruit par domaine. Le modèle exploite les domaines moins bruités pour reconstruire les domaines plus bruités, permettant de modéliser les données manquantes comme du bruit pur et d'obtenir une configuration flexible des domaines condition et cible. Enfin, nous introduisons TD-Paint, un modèle d'inpainting basé sur la diffusion améliorant la vitesse de génération et à réduire la charge de calcul associée à la génération. Notre étude révèle que les modèles d'inpainting par diffusion souffrent d'une désynchronisation entre génération et conditionnement. Les solutions existantes, reposant sur le rééchantillonnage ou des régularisations supplémentaires, augmentent la complexité computationnelle. TD-Paint résout ce problème en modélisant des niveaux de bruit variables au niveau des pixels, permettant une utilisation efficace de la condition dès le début du processusThis thesis explores multi-modal generation and semi-supervised learning, addressing two critical challenges: supporting flexible configurations of input and output across multiple domains, and developing efficient training strategies for semi-supervised data settings. As artificial intelligence systems advance, there is growing need for models that can flexibly integrate and generate multiple modalities, mirroring human cognitive abilities. Conventional deep learning systems often struggle when deviating from their training configuration, which occurs when certain modalities are unavailable in real-world applications. For instance, in medical settings, patients might not undergo all possible scans for a comprehensive analysis system. Additionally, obtaining finer control over generated modalities is crucial for enhancing generation capabilities and providing richer contextual information. As the number of domains increases, obtaining simultaneous supervision across all domains becomes increasingly challenging. We focus on multi-domain translation in a semi-supervised setting, extending the classical domain translation paradigm. Rather than addressing specific translation directions or limiting translations to domain pairs, we develop methods facilitating translations between any possible domain configurations, determined at test time. The semi-supervised aspect reflects real-world scenarios where complete data annotation is often infeasible or prohibitively expensive. Our work explores three main areas: (1) studying latent space regularization functions to enhance domain translation learning with limited supervision, (2) examining the scalability and flexibility of diffusion-based translation models, and (3) improving the generation speed of diffusion-based inpainting models. First, we propose LSM, a semi-supervised translation framework leveraging additional input and structured output data to regularize inter-domain and intra-domain dependencies. Second, we develop MDD, a novel diffusion-based multi-domain translation semi-supervised framework. MDD shifts the classical reconstruction loss of diffusion models to a translation loss by modeling different noise levels per domain. The model leverages less noisy domains to reconstruct noisier ones, modeling missing data from the semi-supervised setting as pure noise and enabling flexible configuration of condition and target domains. Finally, we introduce TD-Paint, a novel diffusion-based inpainting model improving generation speed and reducing computational burden. Through investigation of the generation sampling process, we observe that diffusion-based inpainting models suffer from unsynchronized generation and conditioning. Existing models often rely on resampling steps or additional regularization losses to realign condition and generation, increasing time and computational complexity. TD-Paint addresses this by modeling variable noise levels at the pixel level, enabling efficient use of the condition from the generation onset
3
Wu, Fei. "An online domain-based Portuguese-Chinese machine translation system". Thesis, University of Macau, 1999. http://umaclib3.umac.mo/record=b1636999.
Testo completo
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Chinea, Ríos Mara. "Advanced techniques for domain adaptation in Statistical Machine Translation". Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/117611.
Testo completoAbstract (sommario):
[ES] La Traducción Automática Estadística es un sup-campo de la lingüística computacional que investiga como emplear los ordenadores en el proceso de traducción de un texto de un lenguaje humano a otro. La traducción automática estadística es el enfoque más popular que se emplea para construir estos sistemas de traducción automáticos. La calidad de dichos sistemas depende en gran medida de los ejemplos de traducción que se emplean durante los procesos de entrenamiento y adaptación de los modelos. Los conjuntos de datos empleados son obtenidos a partir de una gran variedad de fuentes y en muchos casos puede que no tengamos a mano los datos más adecuados para un dominio específico. Dado este problema de carencia de datos, la idea principal para solucionarlo es encontrar aquellos conjuntos de datos más adecuados para entrenar o adaptar un sistema de traducción. En este sentido, esta tesis propone un conjunto de técnicas de selección de datos que identifican los datos bilingües más relevantes para una tarea extraídos de un gran conjunto de datos.
Como primer paso en esta tesis, las técnicas de selección de datos son aplicadas para mejorar la calidad de la traducción de los sistemas de traducción bajo el paradigma basado en frases. Estas técnicas se basan en el concepto de representación continua de las palabras o las oraciones en un espacio vectorial. Los resultados experimentales demuestran que las técnicas utilizadas son efectivas para diferentes lenguajes y dominios.
El paradigma de Traducción Automática Neuronal también fue aplicado en esta tesis. Dentro de este paradigma, investigamos la aplicación que pueden tener las técnicas de selección de datos anteriormente validadas en el paradigma basado en frases. El trabajo realizado se centró en la utilización de dos tareas diferentes de adaptación del sistema. Por un lado, investigamos cómo aumentar la calidad de traducción del sistema, aumentando el tamaño del conjunto de entrenamiento. Por otro lado, el método de selección de datos se empleó para crear un conjunto de datos sintéticos. Los experimentos se realizaron para diferentes dominios y los resultados de traducción obtenidos son convincentes para ambas tareas.
Finalmente, cabe señalar que las técnicas desarrolladas y presentadas a lo largo de esta tesis pueden implementarse fácilmente dentro de un escenario de traducción real.[CAT] La Traducció Automàtica Estadística és un sup-camp de la lingüística computacional que investiga com emprar els ordinadors en el procés de traducció d'un text d'un llenguatge humà a un altre. La traducció automàtica estadística és l'enfocament més popular que s'empra per a construir aquests sistemes de traducció automàtics. La qualitat d'aquests sistemes depèn en gran mesura dels exemples de traducció que s'empren durant els processos d'entrenament i adaptació dels models. Els conjunts de dades emprades són obtinguts a partir d'una gran varietat de fonts i en molts casos pot ser que no tinguem a mà les dades més adequades per a un domini específic. Donat aquest problema de manca de dades, la idea principal per a solucionar-ho és trobar aquells conjunts de dades més adequades per a entrenar o adaptar un sistema de traducció. En aquest sentit, aquesta tesi proposa un conjunt de tècniques de selecció de dades que identifiquen les dades bilingües més rellevants per a una tasca extrets d'un gran conjunt de dades. Com a primer pas en aquesta tesi, les tècniques de selecció de dades són aplicades per a millorar la qualitat de la traducció dels sistemes de traducció sota el paradigma basat en frases. Aquestes tècniques es basen en el concepte de representació contínua de les paraules o les oracions en un espai vectorial. Els resultats experimentals demostren que les tècniques utilitzades són efectives per a diferents llenguatges i dominis. El paradigma de Traducció Automàtica Neuronal també va ser aplicat en aquesta tesi. Dins d'aquest paradigma, investiguem l'aplicació que poden tenir les tècniques de selecció de dades anteriorment validades en el paradigma basat en frases. El treball realitzat es va centrar en la utilització de dues tasques diferents. D'una banda, investiguem com augmentar la qualitat de traducció del sistema, augmentant la grandària del conjunt d'entrenament. D'altra banda, el mètode de selecció de dades es va emprar per a crear un conjunt de dades sintètiques. Els experiments es van realitzar per a diferents dominis i els resultats de traducció obtinguts són convincents per a ambdues tasques. Finalment, cal assenyalar que les tècniques desenvolupades i presentades al llarg d'aquesta tesi poden implementar-se fàcilment dins d'un escenari de traducció real.
[EN] La Traducció Automàtica Estadística és un sup-camp de la lingüística computacional que investiga com emprar els ordinadors en el procés de traducció d'un text d'un llenguatge humà a un altre. La traducció automàtica estadística és l'enfocament més popular que s'empra per a construir aquests sistemes de traducció automàtics. La qualitat d'aquests sistemes depèn en gran mesura dels exemples de traducció que s'empren durant els processos d'entrenament i adaptació dels models. Els conjunts de dades emprades són obtinguts a partir d'una gran varietat de fonts i en molts casos pot ser que no tinguem a mà les dades més adequades per a un domini específic. Donat aquest problema de manca de dades, la idea principal per a solucionar-ho és trobar aquells conjunts de dades més adequades per a entrenar o adaptar un sistema de traducció. En aquest sentit, aquesta tesi proposa un conjunt de tècniques de selecció de dades que identifiquen les dades bilingües més rellevants per a una tasca extrets d'un gran conjunt de dades. Com a primer pas en aquesta tesi, les tècniques de selecció de dades són aplicades per a millorar la qualitat de la traducció dels sistemes de traducció sota el paradigma basat en frases. Aquestes tècniques es basen en el concepte de representació contínua de les paraules o les oracions en un espai vectorial. Els resultats experimentals demostren que les tècniques utilitzades són efectives per a diferents llenguatges i dominis. El paradigma de Traducció Automàtica Neuronal també va ser aplicat en aquesta tesi. Dins d'aquest paradigma, investiguem l'aplicació que poden tenir les tècniques de selecció de dades anteriorment validades en el paradigma basat en frases. El treball realitzat es va centrar en la utilització de dues tasques diferents d'adaptació del sistema. D'una banda, investiguem com augmentar la qualitat de traducció del sistema, augmentant la grandària del conjunt d'entrenament. D'altra banda, el mètode de selecció de dades es va emprar per a crear un conjunt de dades sintètiques. Els experiments es van realitzar per a diferents dominis i els resultats de traducció obtinguts són convincents per a ambdues tasques. Finalment, cal assenyalar que les tècniques desenvolupades i presentades al llarg d'aquesta tesi poden implementar-se fàcilment dins d'un escenari de traducció real.
Chinea Ríos, M. (2019). Advanced techniques for domain adaptation in Statistical Machine Translation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/117611
TESIS
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Farajian, Mohammad Amin. "Online Adaptive Neural Machine Translation: from single- to multi-domain scenarios". Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/367944.
Testo completoAbstract (sommario):
In this thesis we investigate methods for deploying machine translation (MT) in real-world application scenarios related to the use of MT in computer assisted translation (CAT), where human translators post-edit MT outputs. In particular, we investigate (in chronological order) MT adaptation under two working conditions: single-domain and multi-domain. In the former, we assume that MT receives requests by a single user working on a single domain, while in the latter we assume the MT system to receive requests i) from multiple users working on different domains, ii) with no predefined order, and iii) without domain information. In the single-domain case, we first focus on word alignment, a core component of online adaptive phrase-based MT (PBMT) that is crucial for extracting features from a post-edited segment. In particular, we concentrate on improving word alignment in presence of out-of-vocabulary words observed in the source sentences or introduced by the post-editor. In the multi-domain scenario we turned our focus to the neural MT (NMT) paradigm. In particular, we introduce a scalable solution that adapts on-the-fly a generic NMT model to each incoming translation request. It relies on a procedure that locally fine-tunes the model to each input sentence using samples retrieved from a pool of parallel data. Our instance-based adaptation uses a more general formulation of the log-likelihood approach to control the contribution of relevant and irrelevant words during model update. Finally, we test our approach on a simulated continuous learning setting, where the system receives user feedback under form of post-editing.
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Farajian, Mohammad Amin. "Online Adaptive Neural Machine Translation: from single- to multi-domain scenarios". Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2921/1/PhD_Thesis_Amin.pdf.
Testo completoAbstract (sommario):
In this thesis we investigate methods for deploying machine translation (MT) in real-world application scenarios related to the use of MT in computer assisted translation (CAT), where human translators post-edit MT outputs. In particular, we investigate (in chronological order) MT adaptation under two working conditions: single-domain and multi-domain. In the former, we assume that MT receives requests by a single user working on a single domain, while in the latter we assume the MT system to receive requests i) from multiple users working on different domains, ii) with no predefined order, and iii) without domain information. In the single-domain case, we first focus on word alignment, a core component of online adaptive phrase-based MT (PBMT) that is crucial for extracting features from a post-edited segment. In particular, we concentrate on improving word alignment in presence of out-of-vocabulary words observed in the source sentences or introduced by the post-editor. In the multi-domain scenario we turned our focus to the neural MT (NMT) paradigm. In particular, we introduce a scalable solution that adapts on-the-fly a generic NMT model to each incoming translation request. It relies on a procedure that locally fine-tunes the model to each input sentence using samples retrieved from a pool of parallel data. Our instance-based adaptation uses a more general formulation of the log-likelihood approach to control the contribution of relevant and irrelevant words during model update. Finally, we test our approach on a simulated continuous learning setting, where the system receives user feedback under form of post-editing.
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Mansour, Saab Verfasser], Hermann [Akademischer Betreuer] [Ney e Khalil [Akademischer Betreuer] Sima'an. "Domain adaptation for statistical machine translation / Saab Mansour ; Hermann Ney, Khalil Sima'an". Aachen : Universitätsbibliothek der RWTH Aachen, 2017. http://d-nb.info/1170780180/34.
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Laranjeira, Bruno Rezende. "On the application of focused crawling for statistical machine translation domain adaptation". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/117259.
Testo completoAbstract (sommario):
O treinamento de sistemas de Tradução de Máquina baseada em Estatística (TME) é bastante dependente da disponibilidade de corpora paralelos. Entretanto, este tipo de recurso costuma ser difícil de ser encontrado, especialmente quando lida com idiomas com poucos recursos ou com tópicos muito específicos, como, por exemplo, dermatologia. Para contornar esta situação, uma possibilidade é utilizar corpora comparáveis, que são recursos muito mais abundantes. Um modo de adquirir corpora comparáveis é a aplicação de algoritmos de Coleta Focada (CF). Neste trabalho, são propostas novas abordagens para CF, algumas baseadas em n-gramas e outras no poder expressivo das expressões multipalavra. Também são avaliadas a viabilidade do uso de CF para realização de adaptação de domínio para sistemas genéricos de TME e se há alguma correlação entre a qualidade dos algoritmos de CF e dos sistemas de TME que podem ser construídos a partir dos respectivos dados coletados. Os resultados indicam que algoritmos de CF podem ser bons meios para adquirir corpora comparáveis para realizar adaptação de domínio para TME e que há uma correlação entre a qualidade dos dois processos.Statistical Machine Translation (SMT) is highly dependent on the availability of parallel corpora for training. However, these kinds of resource may be hard to be found, especially when dealing with under-resourced languages or very specific domains, like the dermatology. For working this situation around, one possibility is the use of comparable corpora, which are much more abundant resources. One way of acquiring comparable corpora is to apply Focused Crawling (FC) algorithms. In this work we propose novel approach for FC algorithms, some based on n-grams and other on the expressive power of multiword expressions. We also assess the viability of using FC for performing domain adaptations for generic SMT systems and whether there is a correlation between the quality of the FC algorithms and of the SMT systems that can be built with its collected data. Results indicate that the use of FCs is, indeed, a good way for acquiring comparable corpora for SMT domain adaptation and that there is a correlation between the qualities of both processes.
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Mansour, Saab [Verfasser], Hermann [Akademischer Betreuer] Ney e Khalil [Akademischer Betreuer] Sima'an. "Domain adaptation for statistical machine translation / Saab Mansour ; Hermann Ney, Khalil Sima'an". Aachen : Universitätsbibliothek der RWTH Aachen, 2017. http://d-nb.info/1170780180/34.
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Pizzati, Fabio <1993>. "Exploring domain-informed and physics-guided learning in image-to-image translation". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10499/1/pizzati_fabio_tesi.pdf.
Testo completoAbstract (sommario):
Image-to-image (i2i) translation networks can generate fake images beneficial for many applications in augmented reality, computer graphics, and robotics. However, they require large scale datasets and high contextual understanding to be trained correctly. In this thesis, we propose strategies for solving these problems, improving performances of i2i translation networks by using domain- or physics-related priors. The thesis is divided into two parts. In Part I, we exploit human abstraction capabilities to identify existing relationships in images, thus defining domains that can be leveraged to improve data usage efficiency. We use additional domain-related information to train networks on web-crawled data, hallucinate scenarios unseen during training, and perform few-shot learning. In Part II, we instead rely on physics priors. First, we combine realistic physics-based rendering with generative networks to boost outputs realism and controllability. Then, we exploit naive physical guidance to drive a manifold reorganization, which allowed generating continuous conditions such as timelapses.
Libri sul tema "Domain translation"
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Kaźmierczak, Marta. Przekład w kręgu intertekstualności: Na materiale tłumaczeń poezji Bolesława Leśmiana = [Perevod v krugu intertekstualʹnosti] = Translation in the domain of intertextuality. Warszawa: Instytut Lingwistyki Stosowanej Uniwersytetu Warszawskiego, 2012.
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Yŏn'guwŏn, Han'guk Chŏnja T'ongsin. Ŭngyong t'ŭkhwa Han-Chung-Yŏng chadong pŏnyŏk kisul kaebal e kwanhan yŏn'gu =: Domain customized machine translation technology development for Korean, Chinese, English. [Kyŏnggi-do Kwach'ŏn-si]: Chisik Kyŏngjebu, 2009.
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Garzone, G. Domain-specific English and language mediation in professional and institutional settings. Milano: Arcipelago, 2003.
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Montgomery, L. M. Le Domaine des peupliers. Montréal: Québec/Amérique, 1994.
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1939-, Memon Muhammad Umar, a cura di. Domains of fear and desire: Urdu stories. Toronto, Ontario: TSAR, 1992.
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1949-, Rioux Hélène, a cura di. Anne au Domaine des Peupliers. Charlottetown, P.E.I: Ragweed Press, 1989.
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Montgomery, L. M. Anne au Domaine des peupliers: Roman. Charlottetown, Î.-P.-É: Ragweed Press, 1989.
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Montgomery, L. M. Anne au Domaine des peupliers: Roman. Montréal: Québec/Amérique, 1989.
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Haroutyunian, Sona, e Dario Miccoli. Orienti migranti: tra letteratura e traduzione. Venice: Fondazione Università Ca’ Foscari, 2020. http://dx.doi.org/10.30687/978-88-6969-499-8.
Testo completoAbstract (sommario):
The book series, edited by Nicoletta Pesaro and sponsored by the Department of Asian and North African Studies, aims to give voice to a time-honoured branch of theoretical and practical research across the disciplines and research domains within the Department. The series aims to establish a platform for scholarly discussion and a space for international dialogue on the translation of Asian and North African languages. In doing so, the project aims to observe and verify the translingual and transcultural dynamics triggered by translation from and into said ‘languages-cultures’, as well as to identify and explore the deep cultural mechanisms and structures involved in interethnic behaviours and relationships. Translation is also a major research tool in the humanities. As a matter of fact, a hermeneutic potential in terms of cultural mediation is inherent in translation activities and in the reflection on translation: it is precisely this potential that allows scholars, in both their research and dissemination work, to bring to the surface the interethnic and intercultural dynamics regulating the relationships between civilisations, both diachronically and synchronically. The project is a continuation and a development of the research carried out in recent years by the former Department of East Asian Studies – now Department of Asian and North African Studies – of Ca’ Foscari University of Venice through a series of initiatives organised by the research group on the translation of Asian languages “Laboratorio sulla Traduzione delle Lingue orientali” (Laboratori sulle lingue orientali). Such activities involved periodical meetings on translation, whose objective was to introduce and discuss specific issues in translation from and into Asian languages, as well as several international events (workshops, conferences, and symposia).
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A, Constas Mark, e Sternberg Robert J, a cura di. Translating theory and research into educational practice: Developments in content domains, large scale reform, and intellectual capacity. Mahwah, NJ: Lawrence Erlbaum Associates, 2006.
Cerca il testo completoCapitoli di libri sul tema "Domain translation"
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Karatsiolis, Savvas, Christos N. Schizas e Nicolai Petkov. "Modular Domain-to-Domain Translation Network". In Artificial Neural Networks and Machine Learning – ICANN 2018, 425–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_42.
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Katzir, Oren, Dani Lischinski e Daniel Cohen-Or. "Cross-Domain Cascaded Deep Translation". In Computer Vision – ECCV 2020, 673–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58536-5_40.
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Noordman, Leo G. M., Wietske Vonk e Wim H. G. Simons. "Knowledge representation in the domain of economics". In Text, Translation, Computational Processing, 235–60. Berlin, New York: DE GRUYTER MOUTON, 2000. http://dx.doi.org/10.1515/9783110826005.235.
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Speranza, Giulia, e Johanna Monti. "Chapter 3. Evaluating the Italian-English machine translation quality of MWUs in the domain of archaeology". In Current Issues in Linguistic Theory, 40–56. Amsterdam: John Benjamins Publishing Company, 2024. http://dx.doi.org/10.1075/cilt.366.03spe.
Testo completoAbstract (sommario):
Multiword units (MWUs) represent a challenging and problematic linguistic issue in the field of Natural Language Processing (NLP) due to their idiosyncratic nature. This paper investigates the quality of Neural Machine Translation (NMT) outputs when dealing with MWUs in the domain of archaeology. As a case study, a dataset of 100 MWUs is used as a Gold Standard to evaluate out-of-context and in-context translation outputs from three state-of-the-art NMT systems for the Italian-English language pair: Google Translate, DeepL, and Microsoft Bing Translator. MT outputs are manually evaluated with reference to the Gold Standard, namely out-of-context and in-context human English translations of the selected 100 MWUs. Results show that terminology is still a problematic category for MT quality and that MWUs translation may vary, and sometimes even improve, when further context is provided.
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Livbjerg, Inge, e Inger M. Mees. "Patterns of dictionary use in non-domain-specific translation". In Benjamins Translation Library, 123–36. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/btl.45.11liv.
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Biggerstaff, Ted J. "Control Localization in Domain Specific Translation". In Software Reuse: Methods, Techniques, and Tools, 153–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46020-9_11.
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Murez, Zak, Soheil Kolouri, David Kriegman, Ravi Ramamoorthi e Kyungnam Kim. "Domain Adaptation via Image to Image Translation". In Domain Adaptation in Computer Vision with Deep Learning, 117–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45529-3_7.
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Sapiro, Gisèle. "The Sociology of Translation: A New Research Domain". In A Companion to Translation Studies, 82–94. Oxford, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118613504.ch6.
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Royer, Amélie, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole e Kevin Murphy. "XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings". In Domain Adaptation for Visual Understanding, 33–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30671-7_3.
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Yang, Manzhi, Huaping Zhang, Chenxi Yu e Guotong Geng. "Continual Domain Adaption for Neural Machine Translation". In Communications in Computer and Information Science, 427–39. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8145-8_33.
Testo completoAtti di convegni sul tema "Domain translation"
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Hendy, Amr, Mohamed Abdelghaffar, Mohamed Afify e Ahmed Y. Tawfik. "Domain Specific Sub-network for Multi-Domain Neural Machine Translation". In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), 351–56. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-short.43.
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You, WangJie, Pei Guo, Juntao Li, Kehai Chen e Min Zhang. "Efficient Domain Adaptation for Non-Autoregressive Machine Translation". In Findings of the Association for Computational Linguistics ACL 2024, 13657–70. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.810.
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Bhattacharjee, Soham, Baban Gain e Asif Ekbal. "Domain Dynamics: Evaluating Large Language Models in English-Hindi Translation". In Proceedings of the Ninth Conference on Machine Translation, 341–54. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.wmt-1.27.
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Hu, Tianxiang, Pei Zhang, Baosong Yang, Jun Xie, Derek F. Wong e Rui Wang. "Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning". In Findings of the Association for Computational Linguistics: EMNLP 2024, 5726–46. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.328.
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Luo, Yuanchang, Zhanglin Wu, Daimeng Wei, Hengchao Shang, Zongyao Li, Jiaxin Guo, Zhiqiang Rao et al. "Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain". In Proceedings of the Ninth Conference on Machine Translation, 949–54. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.wmt-1.93.
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Vogel, Stephan. "Speech-translation: from domain-limited to domain-unlimited translation tasks". In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2007. http://dx.doi.org/10.1109/asru.2007.4430141.
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Lin, Jianxin, Yingce Xia, Yijun Wang, Tao Qin e Zhibo Chen. "Image-to-Image Translation with Multi-Path Consistency Regularization". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/413.
Testo completoAbstract (sommario):
Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from a source domain to a target domain as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images; the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version. In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation from source domain to target domain and indirect translation from source domain to an auxiliary domain to target domain, to regularize training. For multi-domain translation (at least, three) which focuses on building translation models between any two domains, at each training iteration, we randomly select three domains, set them respectively as the source, auxiliary and target domains, build the multi-path consistency loss and optimize the network. For two-domain translation, we need to introduce an additional auxiliary domain and construct the multi-path consistency loss. We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-to-face translation, paint-to-photo translation, and de-raining/de-noising translation.
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Ala, Hema, Vandan Mujadia e Dipti Misra Sharma. "Domain Adaptation for Hindi-Telugu Machine Translation using Domain Specific Back Translation". In International Conference Recent Advances in Natural Language Processing. INCOMA Ltd. Shoumen, BULGARIA, 2021. http://dx.doi.org/10.26615/978-954-452-072-4_004.
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Sokova, Daria, e Cristina Toledo-Báez. "Linguistic Complexity in Domain-Specific Neural Machine Translation". In New Trends in Translation and Technology Conference 2024, 191–200. INCOMA Ltd. Shoumen, BULGARIA, 2024. http://dx.doi.org/10.26615/issn.2815-4711.2024_015.
Testo completoAbstract (sommario):
This study investigates the impact of linguistic complexity in domain-specific data on NMT performance. Using a data selection approach, we created a dataset and trained Transformer models both on domain-specific data and on a general dataset. Linguistic complexity analysis reveals that the domain-specific dataset exhibits notable linguistic complexity, with syntactic, lexical, and textual characteristics posing challenges for NMT. Training models on this complex data resulted in lower translation quality compared to models trained on general data, particularly evident when translating into a morphologically rich language like Russian. These findings may suggest that linguistic complexity in domain-specific data can present challenges for NMT performance, making training computationally intense and hindering the model’s ability to learn accurate representations. Our study emphasizes the importance of considering data characteristics in NMT training, especially in low-resource scenarios. Keywords: Domain-specific Neural Machine Translation · Data Complexity · Lexical Characteristics.
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Wei, Hao-Ran, Zhirui Zhang, Boxing Chen e Weihua Luo. "Iterative Domain-Repaired Back-Translation". In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.474.
Testo completoRapporti di organizzazioni sul tema "Domain translation"
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Micher, Jeffrey C. Improving Domain-specific Machine Translation by Constraining the Language Model. Fort Belvoir, VA: Defense Technical Information Center, luglio 2012. http://dx.doi.org/10.21236/ada568649.
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Lavoie, Benoit, Michael White e Tanya Korelsky. Learning Domain-Specific Transfer Rules: An Experiment with Korean to English Translation. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2002. http://dx.doi.org/10.21236/ada457732.
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Shaver, Amber, Hayam Megally, Sean Boynes, Tooka Zokaie, Nithya Puttige Ramesh, Don Clermont e Annaliese Cothron. Illustrating the Role of Dental Journals in the Translational Science Process. American Institute of Dental Public Health, 2022. http://dx.doi.org/10.58677/pqbg1492.
Testo completoAbstract (sommario):
The “17-year odyssey” outlines the research-to-practice pipeline that begins with setting priorities for funding research, publishing and peer review, research synthesis, developing guidelines for evidence-based practice, then finally as applied practice. Few studies have focused on the implementation gap within oral health domain. In this report, AIDPH outlines barriers within the dental peer-reviewed publication process that slow the translation of research to clinical implementation while offering strategic recommendations to expedite this pipeline. Using a multi-method research approach, this publication illustrates the role of peer-reviewed publications in the oral health translation process. Strategic recommendations include: 1. Evaluate opportunities to reduce funding barriers. 2. Broaden access to peer-reviewed information 3. Increase funding for and publication of replication studies 4. Explore innovative technologies in clinical settings
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Kriegel, Francesco. Learning General Concept Inclusions in Probabilistic Description Logics. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.220.
Testo completoAbstract (sommario):
Probabilistic interpretations consist of a set of interpretations with a shared domain and a measure assigning a probability to each interpretation. Such structures can be obtained as results of repeated experiments, e.g., in biology, psychology, medicine, etc. A translation between probabilistic and crisp description logics is introduced and then utilised to reduce the construction of a base of general concept inclusions of a probabilistic interpretation to the crisp case for which a method for the axiomatisation of a base of GCIs is well-known.
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Chejanovsky, Nor, e Suzanne M. Thiem. Isolation of Baculoviruses with Expanded Spectrum of Action against Lepidopteran Pests. United States Department of Agriculture, dicembre 2002. http://dx.doi.org/10.32747/2002.7586457.bard.
Testo completoAbstract (sommario):
Our long-term goal is to learn to control (expand and restrict) the host range of baculoviruses. In this project our aim was to expand the host range of the prototype baculovirus Autographa cali/arnica nuclear polyhedrosis virus (AcMNPV) towards American and Israeli pests. To achieve this objective we studied AcMNPV infection in the non-permissive hosts L. dispar and s. littoralis (Ld652Y and SL2 cells, respectively) as a model system and the major barriers to viral replication. We isolated recombinant baculoviruses with expanded infectivity towards L. dispar and S. littoralis and tested their infectivity towards other Lepidopteran pests. The restricted host range displayed by baculoviruses constitutes an obstacle to their further implementation in the control of diverse Lepidopteran pests, increasing the development costs. Our work points out that cellular defenses are major role blocks to AcMNPV replication in non- and semi-permissive hosts. Therefore a major determinant ofbaculovirus host range is the ability of the virus to effectively counter cellular defenses of host cells. This is exemplified by our findings showing tliat expressing the viral gene Ldhrf-l overcomes global translation arrest in AcMNPV -infected Ld652Y cells. Our data suggests that Ld652Y cells have two anti-viral defense pathways, because they are subject to global translation arrest when infected with AcMNPV carrying a baculovirus apoptotic suppressor (e.g., wild type AcMNPV carryingp35, or recombinant AcMNPV carrying Opiap, Cpiap. or p49 genes) but apoptose when infected with AcMNPV-Iacking a functional apoptotic suppressor. We have yet to elucidate how hrf-l precludes the translation arrest mechanism(s) in AcMNPV-infected Ld652Y cells. Ribosomal profiles of AcMNPV infected Ld652Y cells suggested that translation initiation is a major control point, but we were unable to rule-out a contribution from a block in translation elongation. Phosphorylation of eIF-2a did not appear to playa role in AcMNPV -induced translation arrest. Mutagenesis studies ofhrf-l suggest that a highly acidic domain plays a role in precluding translation arrest. Our findings indicate that translation arrest may be linked to apoptosis either through common sensors of virus infection or as a consequence of late events in the virus life-cycle that occur only if apoptosis is suppressed. ~ AcMNPV replicates poorly in SL2 cells and induces apoptosis. Our studies in AcMNPV - infected SL2ceils led us to conclude that the steady-state levels of lEI (product of the iel gene, major AcMNPV -transactivator and multifunctional protein) relative to those of the immediate early viral protein lEO, playa critical role in regulating the viral infection. By increasing the IEl\IEO ratio we achieved AcMNPV replication in S. littoralis and we were able to isolate recombinant AcMNPV s that replicated efficiently in S. lifforalis cells and larvae. Our data that indicated that AcMNPV - infection may be regulated by an interaction between IE 1 and lED (of previously unknown function). Indeed, we showed that IE 1 associates with lED by using protein "pull down" and immunoprecipitation approaches High steady state levels of "functional" IE 1 resulted in increased expression of the apoptosis suppressor p35 facilitating AcMNPV -replication in SL2 cells. Finally, we determined that lED accelerates the viral infection in AcMNPV -permissive cells. Our results show that expressing viral genes that are able to overcome the insect-pest defense system enable to expand baculovirus host range. Scientifically, this project highlights the need to further study the anti-viral defenses of invertebrates not only to maximi~e the possibilities for manipulating baculovirus genomes, but to better understand the evolutionary underpinnings of the immune systems of vertebrates towards virus infection.
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Rogers, Aaron. Translational Fidelity of a Eukaryotic Glutaminyl-tRNA Synthetase with an N-terminal Domain Appendage. Portland State University Library, gennaio 2000. http://dx.doi.org/10.15760/etd.2005.
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Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER e Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.
Testo completoAbstract (sommario):
In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: "you COULD, SHOULD or MUST change parameter x". Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.
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Christopher, David A., e Avihai Danon. Plant Adaptation to Light Stress: Genetic Regulatory Mechanisms. United States Department of Agriculture, maggio 2004. http://dx.doi.org/10.32747/2004.7586534.bard.
Testo completoAbstract (sommario):
Original Objectives: 1. Purify and biochemically characterize RB60 orthologs in higher plant chloroplasts; 2. Clone the gene(s) encoding plant RB60 orthologs and determine their structure and expression; 3. Manipulate the expression of RB60; 4. Assay the effects of altered RB60 expression on thylakoid biogenesis and photosynthetic function in plants exposed to different light conditions. In addition, we also examined the gene structure and expression of RB60 orthologs in the non-vascular plant, Physcomitrella patens and cloned the poly(A)-binding protein orthologue (43 kDa RB47-like protein). This protein is believed to a partner that interacts with RB60 to bind to the psbA5' UTR. Thus, to obtain a comprehensive view of RB60 function requires analysis of its biochemical partners such as RB43. Background & Achievements: High levels of sunlight reduce photosynthesis in plants by damaging the photo system II reaction center (PSII) subunits, such as D1 (encoded by the chloroplast tpsbAgene). When the rate of D1 synthesis is less than the rate of photo damage, photo inhibition occurs and plant growth is decreased. Plants use light-activated translation and enhanced psbAmRNA stability to maintain D1 synthesis and replace the photo damaged 01. Despite the importance to photosynthetic capacity, these mechanisms are poorly understood in plants. One intriguing model derived from the algal chloroplast system, Chlamydomonas, implicates the role of three proteins (RB60, RB47, RB38) that bind to the psbAmRNA 5' untranslated leader (5' UTR) in the light to activate translation or enhance mRNA stability. RB60 is the key enzyme, protein D1sulfide isomerase (Pill), that regulates the psbA-RN :Binding proteins (RB's) by way of light-mediated redox potentials generated by the photosystems. However, proteins with these functions have not been described from higher plants. We provided compelling evidence for the existence of RB60, RB47 and RB38 orthologs in the vascular plant, Arabidopsis. Using gel mobility shift, Rnase protection and UV-crosslinking assays, we have shown that a dithiol redox mechanism which resembles a Pill (RB60) activity regulates the interaction of 43- and 30-kDa proteins with a thermolabile stem-loop in the 5' UTR of the psbAmRNA from Arabidopsis. We discovered, in Arabidopsis, the PD1 gene family consists of II members that differ in polypeptide length from 361 to 566 amino acids, presence of signal peptides, KDEL motifs, and the number and positions of thioredoxin domains. PD1's catalyze the reversible formation an disomerization of disulfide bonds necessary for the proper folding, assembly, activity, and secretion of numerous enzymes and structural proteins. PD1's have also evolved novel cellular redox functions, as single enzymes and as subunits of protein complexes in organelles. We provide evidence that at least one Pill is localized to the chloroplast. We have used PDI-specific polyclonal and monoclonal antisera to characterize the PD1 (55 kDa) in the chloroplast that is unevenly distributed between the stroma and pellet (containing membranes, DNA, polysomes, starch), being three-fold more abundant in the pellet phase. PD1-55 levels increase with light intensity and it assembles into a high molecular weight complex of ~230 kDa as determined on native blue gels. In vitro translation of all 11 different Pill's followed by microsomal membrane processing reactions were used to differentiate among PD1's localized in the endoplasmic reticulum or other organelles. These results will provide.1e insights into redox regulatory mechanisms involved in adaptation of the photosynthetic apparatus to light stress. Elucidating the genetic mechanisms and factors regulating chloroplast photosynthetic genes is important for developing strategies to improve photosynthetic efficiency, crop productivity and adaptation to high light environments.
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Ohad, Nir, e Robert Fischer. Regulation of Fertilization-Independent Endosperm Development by Polycomb Proteins. United States Department of Agriculture, gennaio 2004. http://dx.doi.org/10.32747/2004.7695869.bard.
Testo completoAbstract (sommario):
Arabidopsis mutants that we have isolated, encode for fertilization-independent endosperm (fie), fertilization-independent seed2 (fis2) and medea (mea) genes, act in the female gametophyte and allow endosperm to develop without fertilization when mutated. We cloned the FIE and MEA genes and showed that they encode WD and SET domain polycomb (Pc G) proteins, respectively. Homologous proteins of FIE and MEA in other organisms are known to regulate gene transcription by modulating chromatin structure. Based on our results, we proposed a model whereby both FIE and MEA interact to suppress transcription of regulatory genes. These genes are transcribed only at proper developmental stages, as in the central cell of the female gametophyte after fertilization, thus activating endosperm development. To test our model, the following questions were addressed: What is the Composition and Function of the Polycomb Complex? Molecular, biochemical, genetic and genomic approaches were offered to identify members of the complex, analyze their interactions, and understand their function. What is the Temporal and Spatial Pattern of Polycomb Proteins Accumulation? The use of transgenic plants expressing tagged FIE and MEA polypeptides as well as specific antibodies were proposed to localize the endogenous polycomb complex. How is Polycomb Protein Activity Controlled? To understand the molecular mechanism controlling the accumulation of FIE protein, transgenic plants as well as molecular approaches were proposed to determine whether FIE is regulated at the translational or posttranslational levels. The objectives of our research program have been accomplished and the results obtained exceeded our expectation. Our results reveal that fie and mea mutations cause parent-of-origin effects on seed development by distinct mechanisms (Publication 1). Moreover our data show that FIE has additional functions besides controlling the development of the female gametophyte. Using transgenic lines in which FIE was not expressed or the protein level was reduced during different developmental stages enabled us for the first time to explore FIE function during sporophyte development (Publication 2 and 3). Our results are consistent with the hypothesis that FIE, a single copy gene in the Arabidopsis genome, represses multiple developmental pathways (i.e., endosperm, embryogenesis, shot formation and flowering). Furthermore, we identified FIE target genes, including key transcription factors known to promote flowering (AG and LFY) as well as shoot and leaf formation (KNAT1) (Publication 2 and 3), thus demonstrating that in plants, as in mammals and insects, PcG proteins control expression of homeobox genes. Using the Yeast two hybrid system and pull-down assays we demonstrated that FIE protein interact with MEA via the N-terminal region (Publication 1). Moreover, CURLY LEAF protein, an additional member of the SET domain family interacts with FIE as well. The overlapping expression patterns of FIE, with ether MEA or CLF and their common mutant phenotypes, demonstrate the versatility of FIE function. FIE association with different SET domain polycomb proteins, results in differential regulation of gene expression throughout the plant life cycle (Publication 3). In vitro interaction assays we have recently performed demonstrated that FIE interacts with the cell cycle regulatory component Retinobalsoma protein (pRb) (Publication 4). These results illuminate the potential mechanism by which FIE may restrain embryo sac central cell division, at least partly, through interaction with, and suppression of pRb-regulated genes. The results of this program generated new information about the initiation of reproductive development and expanded our understanding of how PcG proteins regulate developmental programs along the plant life cycle. The tools and information obtained in this program will lead to novel strategies which will allow to mange crop plants and to increase crop production.
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McClure, Michael A., Yitzhak Spiegel, David M. Bird, R. Salomon e R. H. C. Curtis. Functional Analysis of Root-Knot Nematode Surface Coat Proteins to Develop Rational Targets for Plantibodies. United States Department of Agriculture, ottobre 2001. http://dx.doi.org/10.32747/2001.7575284.bard.
Testo completoAbstract (sommario):
The goal of this research was to provide a better understanding of the interface between root-knot nematodes, Meloidogyne spp., and their host in order to develop rational targets for plantibodies and other novel methods of nematode control directed against the nematode surface coat (SC). Specific objectives were: 1. To produce additional monoclonal SC antibodies for use in Objectives 2, 3, and 4 and as candidates for development of plantibodies. 2. To determine the production and distribution of SC proteins during the infection process. 3. To use biochemical and immunological methods to perturbate the root-knot nematode SC in order to identify SC components that will serve as targets for rationally designed plantibodies. 4. To develop SC-mutant nematodes as additional tools for defining the role of the SC during infection. The external cuticular layer of nematodes is the epicuticle. In many nematodes, it is covered by a fuzzy material termed "surface coat" (SC). Since the SC is the outermost layer, it may playa role in the interaction between the nematode and its surroundings during all life stages in soil and during pathogenesis. The SC is composed mainly of proteins, carbohydrates (which can be part of glycoproteins), and lipids. SC proteins and glycoproteins have been labeled and extracted from preparasitic second-stage juveniles and adult females of Meloidogyne and specific antibodies have been raised against surface antigens. Antibodies can be used to gain more information about surface function and to isolate genes encoding for surface antigens. Characterization of surface antigens and their roles in different life-stages may be an important step towards the development of alternative control. Nevertheless, the role of the plant- parasitic nematode's surface in plant-nematode interaction is still not understood. Carbohydrates or carbohydrate-recognition domains (CROs) on the nematode surface may interact with CROs or carbohydrate molecules, on root surfaces or exudates, or be active after the nematode has penetrated into the root. Surface antigens undoubtedly play an important role in interactions with microorganisms that adhere to the nematodes. Polyclonal (PC) and monoclonal (MC) antibodies raised against Meloidogyne javanica, M. incognita and other plant-parasitic nematodes, were used to characterize the surface coat and secreted-excreted products of M. javanica and M. incognita. Some of the MC and PC antibodies raised against M. incognita showed cross-reactivity with the surface coat of M. javanica. Further characterization, in planta, of the epitopes recognized by the antibodies, showed that they were present in the parasitic juvenile stages and that the surface coat is shed during root penetration by the nematode and its migration between root cells. At the molecular level, we have followed two lines of experimentation. The first has been to identify genes encoding surface coat (SC) molecules, and we have isolated and characterized a small family of mucin genes from M. incognita. Our second approach has been to study host genes that respond to the nematode, and in particular, to the SC. Our previous work has identified a large suite of genes expressed in Lycopersicon esculentum giant cells, including the partial cDNA clone DB#131, which encodes a serine/threonine protein kinase. Isolation and predicted translation of the mature cDNA revealed a frame shift mutation in the translated region of nematode sensitive plants. By using primers homologous to conserved region of DB#131 we have identified the orthologues from three (nematode-resistant) Lycopersicon peruvianum strains and found that these plants lacked the mutation.