Academic literature on the topic 'Monolingual machine translation'

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Journal articles on the topic "Monolingual machine translation"

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Riezler, Stefan, and Yi Liu. "Query Rewriting Using Monolingual Statistical Machine Translation." Computational Linguistics 36, no. 3 (2010): 569–82. http://dx.doi.org/10.1162/coli_a_00010.

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Long queries often suffer from low recall in Web search due to conjunctive term matching. The chances of matching words in relevant documents can be increased by rewriting query terms into new terms with similar statistical properties. We present a comparison of approaches that deploy user query logs to learn rewrites of query terms into terms from the document space. We show that the best results are achieved by adopting the perspective of bridging the “lexical chasm” between queries and documents by translating from a source language of user queries into a target language of Web documents. W
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Marie, Benjamin, and Atsushi Fujita. "Synthesizing Parallel Data of User-Generated Texts with Zero-Shot Neural Machine Translation." Transactions of the Association for Computational Linguistics 8 (November 2020): 710–25. http://dx.doi.org/10.1162/tacl_a_00341.

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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 b
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IRVINE, ANN, and CHRIS CALLISON-BURCH. "End-to-end statistical machine translation with zero or small parallel texts." Natural Language Engineering 22, no. 4 (2016): 517–48. http://dx.doi.org/10.1017/s1351324916000127.

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AbstractWe use bilingual lexicon induction techniques, which learn translations from monolingual texts in two languages, to build an end-to-end statistical machine translation (SMT) system without the use of any bilingual sentence-aligned parallel corpora. We present detailed analysis of the accuracy of bilingual lexicon induction, and show how a discriminative model can be used to combine various signals of translation equivalence (like contextual similarity, temporal similarity, orthographic similarity and topic similarity). Our discriminative model produces higher accuracy translations than
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Lesatari, Aufa Eka Putri, Arie Ardiyanti, Arie Ardiyanti, Ibnu Asror, and Ibnu Asror. "Phrase Based Statistical Machine Translation Javanese-Indonesian." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 2 (2021): 378. http://dx.doi.org/10.30865/mib.v5i2.2812.

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This research aims to produce a statistical machine translation that can be implemented to perform Javanese-Indonesian translation and to know the influence of the main data sources of statistical machine translation namely parallel corpus and monolingual corpus on the quality of Javanese-Indonesian statistical machine translation. The testing was carried out by gradually adding the quantity of parallel corpus and monolingual corpus to seven configurations of Javanese-Indonesian statistical machine translation. All machine translation configuration experiments were tested with test data totali
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Yu, Lei, Laurent Sartran, Wojciech Stokowiec, et al. "Better Document-Level Machine Translation with Bayes’ Rule." Transactions of the Association for Computational Linguistics 8 (July 2020): 346–60. http://dx.doi.org/10.1162/tacl_a_00319.

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We show that Bayes’ rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents a compelling benefit because parallel documents are not always available. In our formulation, the posterior probability of a candidate translation is the product of the unconditional (prior) probability of the candidate output document and the “reverse translation probability” of translating the candidate output back into the source language. Our proposed model uses a powerful autoregressive language model as the prior on t
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Zhang, Wenbo, Xiao Li, Yating Yang, and Rui Dong. "Pre-Training on Mixed Data for Low-Resource Neural Machine Translation." Information 12, no. 3 (2021): 133. http://dx.doi.org/10.3390/info12030133.

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The pre-training fine-tuning mode has been shown to be effective for low resource neural machine translation. In this mode, pre-training models trained on monolingual data are used to initiate translation models to transfer knowledge from monolingual data into translation models. In recent years, pre-training models usually take sentences with randomly masked words as input, and are trained by predicting these masked words based on unmasked words. In this paper, we propose a new pre-training method that still predicts masked words, but randomly replaces some of the unmasked words in the input
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Wang, Rui. "Neural Network Machine Translation Method Based on Unsupervised Domain Adaptation." Complexity 2020 (December 24, 2020): 1–11. http://dx.doi.org/10.1155/2020/6657344.

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Relying on large-scale parallel corpora, neural machine translation has achieved great success in certain language pairs. However, the acquisition of high-quality parallel corpus is one of the main difficulties in machine translation research. In order to solve this problem, this paper proposes unsupervised domain adaptive neural network machine translation. This method can be trained using only two unrelated monolingual corpora and obtain a good translation result. This article first measures the matching degree of translation rules by adding relevant subject information to the translation ru
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Li, Zhen, Dan Qu, Chaojie Xie, Wenlin Zhang, and Yanxia Li. "Language Model Pre-training Method in Machine Translation Based on Named Entity Recognition." International Journal on Artificial Intelligence Tools 29, no. 07n08 (2020): 2040021. http://dx.doi.org/10.1142/s0218213020400217.

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Neural Machine Translation (NMT) model has become the mainstream technology in machine translation. The supervised neural machine translation model trains with abundant of sentence-level parallel corpora. But for low-resources language or dialect with no such corpus available, it is difficult to achieve good performance. Researchers began to focus on unsupervised neural machine translation (UNMT) that monolingual corpus as training data. UNMT need to construct the language model (LM) which learns semantic information from the monolingual corpus. This paper focuses on the pre-training of LM in
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Yang, Zhen, Wei Chen, Feng Wang, and Bo Xu. "Effectively training neural machine translation models with monolingual data." Neurocomputing 333 (March 2019): 240–47. http://dx.doi.org/10.1016/j.neucom.2018.12.032.

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He, Xiaodong, Mei Yang, Jianfeng Gao, Patrick Nguyen, and Robert Moore. "Improved Monolingual Hypothesis Alignment for Machine Translation System Combination." ACM Transactions on Asian Language Information Processing 8, no. 2 (2009): 1–19. http://dx.doi.org/10.1145/1526252.1526254.

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Dissertations / Theses on the topic "Monolingual machine translation"

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Lindeberg, James. "Simplification of Swedish Text by Monolingual Machine Translation Transformation Rules for Simplified Swedish." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352897.

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Discussions on the concept of ”Klarspråk” and ”Lättläst” are major topics in Sweden at the moment with efforts towards increasing readability of government organization information in the project ”Begriplig Text”. In the field of natural language processing, a lot of efforts have gone into text simplification. This thesis examines the possibility of using hand crafted transfer and generation rules to increase the readability of text through monolingual machine translation. Transfer and generation rules were developed that handle Swedish grammatical structures including explicitivity and word o
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Fomicheva, Marina. "The Role of human reference translation in machine translation evaluation." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/404987.

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Both manual and automatic methods for Machine Translation (MT) evaluation heavily rely on professional human translation. In manual evaluation, human translation is often used instead of the source text in order to avoid the need for bilingual speakers, whereas the majority of automatic evaluation techniques measure string similarity between MT output and a human translation (commonly referred to as candidate and reference translations), assuming that the closer they are, the higher the MT quality. In spite of the crucial role of human reference translation in the assessment of MT quality, its
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Štajner, Sanja. "New data-driven approaches to text simplification." Thesis, University of Wolverhampton, 2016. http://hdl.handle.net/2436/601113.

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Štajner, Sanja. "New data-driven approaches to text simplification." Thesis, University of Wolverhampton, 2015. http://hdl.handle.net/2436/554413.

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Many texts we encounter in our everyday lives are lexically and syntactically very complex. This makes them difficult to understand for people with intellectual or reading impairments, and difficult for various natural language processing systems to process. This motivated the need for text simplification (TS) which transforms texts into their simpler variants. Given that this is still a relatively new research area, many challenges are still remaining. The focus of this thesis is on better understanding the current problems in automatic text simplification (ATS) and proposing new data-driven
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Blanchon, Hervé. "LIDIA-1 : une première maquette vers la TA Interactive "pour tous"." Phd thesis, Grenoble 1, 1994. http://tel.archives-ouvertes.fr/tel-00005079.

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L'objet de cette thèse est la définition et l'étude, au moyen d'une maquette, du concept de Traduction Automatisée Fondée sur le Dialogue pour auteur monolingue. Nous situons d'abord l'objet de cette étude dans une perspective historique en montrant pourquoi les travaux précédents en TAFD n'ont pas (encore) conduit à des systèmes utilisés en pratique. Nous proposons alors le concept de TAFD pour auteur monoligue qui offre de nouvelles perspectives concrètes et permet de poser des questions scientifiques intéressantes. Le but est de permettre à un auteur monolingue de documentation technique de
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Lin, Sheng-Sian, and 林勝賢. "Improving Translation Fluency with a Monolingual Statistical Machine Translation Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/44433665590486418899.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>96<br>While there are many SMT researches for the past tens of years, the performances are far from satisfactory. In translating English to Chinese, for instance, the BLEU Scores (Papineni, 2002) range only between 0.21 and 0.29. Such translation quality is very disfluent for human readers. The goal of the current work is to propose an statistical post-editing model for improving the fluency of translated sentences. The main approaches for improving classical SMT had put much energy on the Translation Model (TM) for a long time. Unfortunately, the classical SMT mode
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Book chapters on the topic "Monolingual machine translation"

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Bashkansky, Guy, and Uzzi Ornan. "Monolingual Translator Workstation." In Machine Translation and the Information Soup. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49478-2_13.

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Wang, Lin, Zhaoxuan Li, Hongyan Zhang, and Wuying Liu. "Monolingual Corpus Driven Vietnamese-Chinese Neural Machine Translation." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_156.

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Soricut, Radu, Kevin Knight, and Daniel Marcu. "Using a Large Monolingual Corpus to Improve Translation Accuracy." In Machine Translation: From Research to Real Users. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45820-4_16.

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Lin, Tracy, Jian-Cheng Wu, and Jason S. Chang. "Extraction of Name and Transliteration in Monolingual and Parallel Corpora." In Machine Translation: From Real Users to Research. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30194-3_20.

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Lam-Adesina, Adenike M., and Gareth J. F. Jones. "Exeter at CLEF 2002: Experiments with Machine Translation for Monolingual and Bilingual Retrieval." In Advances in Cross-Language Information Retrieval. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45237-9_10.

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Pang, Chenxi, Hai Zhao, and Zhongyi Li. "I Can Guess What You Mean: A Monolingual Query Enhancement for Machine Translation." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47674-2_5.

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Lam-Adesina, Adenike M., and Gareth J. F. Jones. "Exeter at CLEF 2003: Experiments with Machine Translation for Monolingual, Bilingual and Multilingual Retrieval." In Comparative Evaluation of Multilingual Information Access Systems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30222-3_26.

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Nikoulina, Vassilina, and Stéphane Clinchant. "Domain Adaptation of Statistical Machine Translation Models with Monolingual Data for Cross Lingual Information Retrieval." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36973-5_80.

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Di Gangi, Mattia A., and Federico Marcello. "Can Monolingual Embeddings Improve Neural Machine Translation?" In Proceedings of the Fourth Italian Conference on Computational Linguistics CLiC-it 2017. Accademia University Press, 2017. http://dx.doi.org/10.4000/books.aaccademia.2396.

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Lu, Bin, Benjamin K. Tsou, Tao Jiang, Jingbo Zhu, and Oi Yee Kwong. "Mining Parallel Knowledge from Comparable Patents." In Ontology Learning and Knowledge Discovery Using the Web. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-625-1.ch013.

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The extracted parallel sentences and technical terms could be a good basis for further acquisition of term relations and the translation of monolingual ontologies, as well as for statistical machine translation systems and other cross-lingual information access applications.
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Conference papers on the topic "Monolingual machine translation"

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Cai, Deng, Yan Wang, Huayang Li, Wai Lam, and Lemao Liu. "Neural Machine Translation with Monolingual Translation Memory." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.567.

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Fomicheva, Marina, and Lucia Specia. "Reference Bias in Monolingual Machine Translation Evaluation." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-2013.

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Franz, Martin, and J. Scott McCarley. "Machine translation and monolingual information retrieval (poster abstract)." In the 22nd annual international ACM SIGIR conference. ACM Press, 1999. http://dx.doi.org/10.1145/312624.312725.

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Sennrich, Rico, Barry Haddow, and Alexandra Birch. "Improving Neural Machine Translation Models with Monolingual Data." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-1009.

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Philip, Jerin, Alexandre Berard, Matthias Gallé, and Laurent Besacier. "Monolingual Adapters for Zero-Shot Neural Machine Translation." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.361.

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Voita, Elena, Rico Sennrich, and Ivan Titov. "Context-Aware Monolingual Repair for Neural Machine Translation." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1081.

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Currey, Anna, Antonio Valerio Miceli Barone, and Kenneth Heafield. "Copied Monolingual Data Improves Low-Resource Neural Machine Translation." In Proceedings of the Second Conference on Machine Translation. Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-4715.

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Currey, Anna, and Kenneth Heafield. "Zero-Resource Neural Machine Translation with Monolingual Pivot Data." In Proceedings of the 3rd Workshop on Neural Generation and Translation. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-5610.

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Zhang, Jiajun, and Chengqing Zong. "Exploiting Source-side Monolingual Data in Neural Machine Translation." In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/d16-1160.

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Bertoldi, Nicola, and Marcello Federico. "Domain adaptation for statistical machine translation with monolingual resources." In the Fourth Workshop. Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1626431.1626468.

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