Relevant bibliographies by topics / Text-To-Text Generation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Text-To-Text Generation'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Text-To-Text Generation"

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V, Pavithra, Rosy S, Srinishanthini R. B, and Prinslin L. "Text-To-Image Generation Using AI." International Journal of Research Publication and Reviews 4, no. 4 (2023): 4932–37. http://dx.doi.org/10.55248/gengpi.234.4.38568.

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Chaddha, Mahima, Sneha Kashid, and Snehal Bhosale Prof Radha Deoghare. "Deep Learning for X-ray Image to Text Generation." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 1679–82. http://dx.doi.org/10.31142/ijtsrd23168.

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Avhad, Pranjali. "WordCanvas: Text-to-Image Generation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32152.

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This project investigates the novel use of stable dif- fusion techniques to generate high-quality images from detailed text descriptions. The combination of natural language under- standing and computer vision in text-to-image conversion opens up new possibilities for content creation and communication. Using cutting-edge stable diffusion models, our project builds a solid foundation for the generation process, which includes tokenization, pre-processing, specialized architecture design, and post-processing techniques. The advantages include eye-catching images, increased user engagement, cont
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He, Yuze, Yushi Bai, Matthieu Lin, et al. "Text-image conditioned diffusion for consistent text-to-3D generation." Computer Aided Geometric Design 111 (June 2024): 102292. http://dx.doi.org/10.1016/j.cagd.2024.102292.

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Yang, Sen, and Yang Liu. "Data-to-text Generation via Planning." Journal of Physics: Conference Series 1827, no. 1 (2021): 012190. http://dx.doi.org/10.1088/1742-6596/1827/1/012190.

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Teng, Zhaopu. "Abstractive summarization of COVID-19 with transfer text-to-text transformer." Applied and Computational Engineering 2, no. 1 (2023): 232–38. http://dx.doi.org/10.54254/2755-2721/2/20220520.

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As a classic problem of Natural Language Processing, summarization provides convenience for studies, research, and daily life. The performance of generation summarization by Natural Language Processing techniques has attracted considerable attention. Meanwhile, COVID-19, a global explosion event, has led to the emergence of a large number of articles and research. The wide variety of articles makes it a perfect realization object for summarization generation tasks. This paper designed and implemented experiments by fine tuning T5 model to get an abstract summarization of COVID-19 literatures.
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Liu, Fangyu, Qianchu Liu, Shruthi Bannur, et al. "Compositional Zero-Shot Domain Transfer with Text-to-Text Models." Transactions of the Association for Computational Linguistics 11 (2023): 1097–113. http://dx.doi.org/10.1162/tacl_a_00585.

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Abstract Label scarcity is a bottleneck for improving task performance in specialized domains. We propose a novel compositional transfer learning framework (DoT51) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from masked language modelling of unlabelled in-domain free text) and task knowledge (from task training on more readily available general-domain data) in a multi-task manner. To improve the transferability of task training, we design a strategy named NLGU: We simultaneously train natural language generation (NLG) for in-domain l
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Chary, Podakanti Satyajith. "Text Generation: Using Markov Model & LSTM Networks to Generate Realistic Text." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1323–27. http://dx.doi.org/10.22214/ijraset.2023.57601.

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Abstract: Text generation plays a crucial role in various natural language processing applications, ranging from creative writing to chatbots. This research delves into the realm of text generation by exploring and comparing two distinct techniques: Markov models and Long Short-Term Memory (LSTM) networks. The study focuses on their ability to generate realistic text within specific styles or genres, providing valuable insights into their respective strengths and limitations. Markov models, rooted in probability theory, and LSTM networks, a type of recurrent neural network, represent contrasti
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Liu, Jingyi. "How to Imagine the World with Text? From Text-to-image Generation View." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 644–50. http://dx.doi.org/10.54097/hset.v39i.6619.

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Words are an effective and convenient way to describe the world, but sometimes what the texts convey may be misunderstood by readers. The expression of pictures is more vivid, easy to understand and has no borders, but creating a painting often takes a long time. Text-to-image makes the two expressions complement each other: It makes every ordinary person a “painter”, so that they can feel the world, express themselves, and create more whimsy through many rich pictures. For this vision, technologists are trying their best to improve image generation models, which enables computers to generate
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Chomphooyod, Peerawat, Atiwong Suchato, Nuengwong Tuaycharoen, and Proadpran Punyabukkana. "English grammar multiple-choice question generation using Text-to-Text Transfer Transformer." Computers and Education: Artificial Intelligence 5 (2023): 100158. http://dx.doi.org/10.1016/j.caeai.2023.100158.

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Dissertations / Theses on the topic "Text-To-Text Generation"

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Konstas, Ioannis. "Joint models for concept-to-text generation." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/8926.

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Much of the data found on the world wide web is in numeric, tabular, or other nontextual format (e.g., weather forecast tables, stock market charts, live sensor feeds), and thus inaccessible to non-experts or laypersons. However, most conventional search engines and natural language processing tools (e.g., summarisers) can only handle textual input. As a result, data in non-textual form remains largely inaccessible. Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input, and holds promise for rendering non-linguistic data widely access
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Gkatzia, Dimitra. "Data-driven approaches to content selection for data-to-text generation." Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/3003.

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Data-to-text systems are powerful in generating reports from data automatically and thus they simplify the presentation of complex data. Rather than presenting data using visualisation techniques, data-to-text systems use human language, which is the most common way for human-human communication. In addition, data-to-text systems can adapt their output content to users’ preferences, background or interests and therefore they can be pleasant for users to interact with. Content selection is an important part of every data-to-text system, because it is the module that decides which from the avail
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Cohen, Aaron Seth 1974. "Automatic generation of fundamental frequency for text-to-speech synthesis." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43501.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.<br>Includes bibliographical references (p. 82-86).<br>by Aaron Seth Cohen.<br>M.Eng.
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Keshtkar, Fazel. "A Computational Approach to the Analysis and Generation of Emotion in Text." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20137.

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Sentiment analysis is a field of computational linguistics involving identification, extraction, and classification of opinions, sentiments, and emotions expressed in natural language. Sentiment classification algorithms aim to identify whether the author of a text has a positive or a negative opinion about a topic. One of the main indicators which help to detect the opinion are the words used in the texts. Needless to say, the sentiments expressed in the texts also depend on the syntactic structure and the discourse context. Supervised machine learning approaches to sentiment classification
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Norris, Jennifer Vivien. "The generation of compound nominals to represent the essence of text : the COMMIX system." Thesis, University of Brighton, 1998. http://hdl.handle.net/10026.1/2453.

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This thesis concerns the COMMIX system, which automatically extracts information on what a text is about, and generates that information in the highly compacted form of compound nominal expressions. The expressions generated are complex and may include novel terms which do not appear themselves in the input text. From the practical point of view, the work is driven by the need for better representations of content: for representations which are shorter and more concise than would appear in an abstract, yet more informative and representative of the actual aboutness than commonly occurs in inde
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Patten, Terry A. "Interpreting systemic grammar as a computational representation : a problem solving approach to text generation." Thesis, University of Edinburgh, 1986. http://hdl.handle.net/1842/19223.

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Hill, Geoffrey. "Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1433597354.

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Faille, Juliette. "Data-Based Natural Language Generation : Evaluation and Explainability." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0305.

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Les modèles de génération de langage naturel (NLG) ont récemment atteint de très hautes performances. Les textes qu'ils produisent sont généralement corrects sur le plan grammatical et syntaxique, ce qui les rend naturels. Bien que leur sens soit correct dans la grande majorité des cas, même les modèles de NLG les plus avancés produisent encore des textes avec des significations partiellement inexactes. Dans cette thèse, en nous concentrons sur le cas particulier des problèmes liés au contenu des textes générés, nous proposons d'évaluer et d'analyser les modèles utilisés dans les tâches de ver
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Pereira, José Casimiro. "Natural language generation in the context of multimodal interaction in Portuguese : Data-to-text based in automatic translation." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/21767.

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Doutoramento em Informática<br>Resumo em português não disponivel<br>To enable the interaction by text and/or speech it is essential that we devise systems capable of translating internal data into sentences or texts that can be shown on screen or heard by users. In this context, it is essential that these natural language generation (NLG) systems provide sentences in the native languages of the users (in our case European Portuguese) and enable an easy development and integration process while providing an output that is perceived as natural. The creation of high quality NLG systems is
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Narayan, Shashi. "Generating and simplifying sentences." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0166/document.

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Selon la représentation d’entrée, cette thèse étudie ces deux types : la génération de texte à partir de représentation de sens et à partir de texte. En la première partie (Génération des phrases), nous étudions comment effectuer la réalisation de surface symbolique à l’aide d’une grammaire robuste et efficace. Cette approche s’appuie sur une grammaire FB-LTAG et prend en entrée des arbres de dépendance peu profondes. La structure d’entrée est utilisée pour filtrer l’espace de recherche initial à l’aide d’un concept de filtrage local par polarité afin de paralléliser les processus. Afin nous p
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Books on the topic "Text-To-Text Generation"

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Jacquie, Hills, ed. Text generation: Writing 11 to 14. Oxford University Press, 2004.

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Text generation: Using discourse strategies and focus constraints to generate natural language text. Cambridge University Press, 1985.

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H, Webb Willyn, ed. Teaching generation text: Using cell phones to enhance learning. Jossey-Bass, 2011.

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Angelo, Adrienne. The fourth generation of French feminist writers (1985-2010): Fictionalized text to fictionalized author. Edwin Mellen Press, 2009.

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van, Heuven Vincent, and Pols, Louis C. W., 1941-, eds. Analysis and synthesis of speech: Strategic research towards high-quality text-to-speech generation. Mouton de Gruyter, 1993.

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Wilcock, G. Interactive Japanese-European text generation: An approach to multilingual export translation based on systemicfunctional grammar. UMIST, 1993.

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1944-, Paine Pamela Fries, ed. The fourth generation of French feminist writers (1985-2010): From fictionalized text to fictionalized author. Edwin Mellen Press, 2009.

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Blinov, Aleksandr, Yuriy Rozhdestvenskiy, Yuriy Marchuk, and Sergey Romashko. Introduction to Linguistics. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1070194.

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The textbook is based on the lecture course "Fundamentals of Linguistics", taught at the Faculty of Philology of Lomonosov Moscow State University. The book introduces students to the system of concepts and terms used by any philological discipline. The purpose of the textbook is to provide theoretical training for students to learn languages and help them master languages practically. The text of the textbook introduces students to the range of problems that are further generalized in the courses "General Linguistics", "Theory of Language", "History of linguistic teachings".&#x0D; Meets the r
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Ivanovskaya, Ol'ga, Ol'ga Eleckaya, and Irina Prischepova. Text pedagogy and psycholinguistics. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1845986.

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The textbook is devoted to the philological foundations of defectological education and psycholinguistics. It takes into account the peculiarities of the professional activity of a teacher — a person who has to use language and speech as tools of his work.&#x0D; Meets the requirements of the federal state educational standards of higher education of the latest generation.&#x0D; For undergraduate students studying in the field of training "Special (defectological) education".
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1943-, Nief Ron, ed. The Mindset lists of American history: From typewriters to text messages, what ten generations of Americans think is normal. John Wiley & Sons, 2011.

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Book chapters on the topic "Text-To-Text Generation"

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Bosma, Wauter, Erwin Marsi, Emiel Krahmer, and Mariët Theune. "Text-to-Text Generation for Question Answering." In Interactive Multi-modal Question-Answering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17525-1_6.

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Barzilay, Regina. "Probabilistic Approaches for Modeling Text Structure and Their Application to Text-to-Text Generation." In Empirical Methods in Natural Language Generation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15573-4_1.

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Stede, Manfred. "From Sentences to Text." In Lexical Semantics and Knowledge Representation in Multilingual Text Generation. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5179-9_10.

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Yang, Diyi, and Lucie Flek. "Towards User-Centric Text-to-Text Generation: A Survey." In Text, Speech, and Dialogue. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83527-9_1.

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Gardent, Claire. "Syntax and Data-to-Text Generation." In Statistical Language and Speech Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11397-5_1.

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Yan, Kun, Lei Ji, Chenfei Wu, et al. "Trace Controlled Text to Image Generation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20059-5_4.

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Din, Altaf U., and Awais Adnan. "Text to Code: Pseudo Code Generation." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34365-1_3.

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Jindal, Rajni, V. Sriram, Vishesh Aggarwal, and Vishesh Jain. "Text to Image Generation Using Gan." In Pervasive Computing and Social Networking. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2840-6_51.

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Pęzik, Piotr, Agnieszka Mikołajczyk, Adam Wawrzyński, Filip Żarnecki, Bartłomiej Nitoń, and Maciej Ogrodniczuk. "Transferable Keyword Extraction and Generation with Text-to-Text Language Models." In Computational Science – ICCS 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36021-3_42.

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Coyne, Bob, Owen Rambow, Julia Hirschberg, and Richard Sproat. "Frame Semantics in Text-to-Scene Generation." In Knowledge-Based and Intelligent Information and Engineering Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15384-6_40.

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Conference papers on the topic "Text-To-Text Generation"

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Kale, Mihir, and Abhinav Rastogi. "Text-to-Text Pre-Training for Data-to-Text Tasks." In Proceedings of the 13th International Conference on Natural Language Generation. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.inlg-1.14.

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Xuyuan, Liang, Tian Lihua, and Li Chen. "TCTG:A Controllable Text Generation Method Using Text to Control Text Generation." In 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP). IEEE, 2021. http://dx.doi.org/10.1109/icsip52628.2021.9688767.

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Kasner, Zdeněk, and Ondřej Dušek. "Data-to-Text Generation with Iterative Text Editing." In Proceedings of the 13th International Conference on Natural Language Generation. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.inlg-1.9.

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Ive, Julia, Pranava Madhyastha, and Lucia Specia. "Deep Copycat Networks for Text-to-Text Generation." 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/d19-1318.

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Ge, Songwei, Taesung Park, Jun-Yan Zhu, and Jia-Bin Huang. "Expressive Text-to-Image Generation with Rich Text." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.00694.

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McKeown, Kathleen. "Query-focused summarization using text-to-text generation." In the 2009 Workshop. Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1708155.1708157.

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Richter, Christian, Yanran Chen, and Steffen Eger. "TUDA-Reproducibility @ ReproGen: Replicability of Human Evaluation of Text-to-Text and Concept-to-Text Generation." In Proceedings of the 14th International Conference on Natural Language Generation. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.inlg-1.32.

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Barzilay, Regina. "Probabilistic approaches for modeling text structure and their application to text-to-text generation." In the 12th European Workshop. Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1610195.1610200.

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Soricut, Radu, and Daniel Marcu. "Towards developing generation algorithms for text-to-text applications." In the 43rd Annual Meeting. Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1219840.1219849.

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Dai, Yaoyao, Benjamin Radford, and Andrew Halterman. "Political Event Coding as Text-to-Text Sequence Generation." In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.case-1.16.

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Reports on the topic "Text-To-Text Generation"

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Wolz, Ursula. An Object Oriented Approach to Content Planning for Text Generation. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada460211.

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Buchanan, Ben, Andrew Lohn, Micah Musser, and Katerina Sedova. Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/2021ca003.

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Growing popular and industry interest in high-performing natural language generation models has led to concerns that such models could be used to generate automated disinformation at scale. This report examines the capabilities of GPT-3--a cutting-edge AI system that writes text--to analyze its potential misuse for disinformation. A model like GPT-3 may be able to help disinformation actors substantially reduce the work necessary to write disinformation while expanding its reach and potentially also its effectiveness.
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Reyes Díaz, Carlos Humberto. Working Paper PUEAA No. 8. CPTPP. Legal Trends. Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.006r.2022.

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Free trade areas (and customs unions) were established in a multilateral level since in Article XXIV of the GATT, and that is the legal minimum from which preferential trade agreements are now built. Some say CPTPP is part of a new generation of Free Trade Agreements because it goes deeper in the integration process. The CPTPP Agreement is a 584-page treaty, a very extensive legal instrument with 30 chapters, so when we talk about legal trends it refers to all 30 chapters at first. But it’s not the idea to explain every chapter in this text, not even just the dispute mechanisms, but the legal
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McDonald, David D., and Marie W. Meteer. From Water to Wine: Generating Natural Language Text from Today's Applications Programs. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada460352.

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Ma, Yue, and Felix Distel. Learning Formal Definitions for Snomed CT from Text. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.193.

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Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. Existing approaches for ontology generation mostly focus on learning superclass or subclass relations and therefore fail to be used to generate Snomed CT definitions. In this paper, we present an approach for the extraction of Snomed CT definitions from natural language texts, based on the distance relation extraction approach. By benefiting from a relatively large amount
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Felix, Juri, and Laura Webb. Use of artificial intelligence in education delivery and assessment. Parliamentary Office of Science and Technology, 2024. http://dx.doi.org/10.58248/pn712.

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This POSTnote considers how artificial intelligence (AI) technologies can be used by educators and learners in schools, colleges and universities. Artificial intelligence technologies that can be used in education have developed rapidly in recent years. This has been driven in part by advancements of generative AI, which is now capable of performing a wide range of tasks including the production of realistic content such as text, images, audio and video. Artificial intelligence tools have the potential to provide different ways of learning and to help educators with lesson planning, marking an
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Foundation models such as ChatGPT through the prism of the UNESCO Recommendation on the Ethics of Artificial Intelligence. UNESCO, 2023. http://dx.doi.org/10.54678/bgiv6160.

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The release into the public domain and massive growth in the user base of artificial intelligence (AI) foundation models for text, images, and audio is fuelling debate about the risks they pose to work, education, scientific research, and democracy, as well as their potential negative impacts on cultural diversity and cross-cultural interactions, among other areas. Foundation models are AI systems that are characterized by the use of very large machine learning models trained on massive unlabelled data sets using considerable compute resources. Examples include large language models (LLMs) suc
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