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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 BiSECT: Learning to Split and Rephrase Sentences with Bitexts ... Kim, Joongwon; Maddela, Mounica; Kriz, Reno. - : arXiv, 2021 BASE Show details 2 BiSECT: Learning to Split and Rephrase Sentences with Bitexts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Callison-Burch, Chris; Kim, Joongwon. - : Underline Science Inc., 2021 BASE Show details 3 Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders Tang, Sunny X.; Kriz, Reno; Cho, Sunghye... In: NPJ Schizophr (2021) BASE Show details 4 Towards a Practically Useful Text Simplification System Kriz, Reno In: Dissertations available from ProQuest (2021) Abstract: While there is a vast amount of text written about nearly any topic, this is often difficult for someone unfamiliar with a specific field to understand. Automated text simplification aims to reduce the complexity of a document, making it more comprehensible to a broader audience. Much of the research in this field has traditionally focused on simplification sub-tasks, such as lexical, syntactic, or sentence-level simplification. However, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many poor transformations; on the other hand, recent neural models, while producing grammatical output, often do not make all needed changes to the original text. In this thesis, I discuss novel approaches for improving lexical and sentence-level simplification systems. Regarding sentence simplification models, after noting that encouraging diversity at inference time leads to significant improvements, I take a closer look at the idea of diversity and perform an exhaustive comparison of diverse decoding techniques on other generation tasks. I also discuss the limitations in the framing of current simplification tasks, which prevent these models from yet being practically useful. Thus, I also propose a retrieval-based reformulation of the problem. Specifically, starting with a document, I identify concepts critical to understanding its content, and then retrieve documents relevant for each concept, re-ranking them based on the desired complexity level. Keyword: Artificial intelligence URL: https://repository.upenn.edu/dissertations/AAI28715495 BASE Hide details 5 Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification ... Kriz, Reno; Sedoc, João; Apidianaki, Marianna. - : arXiv, 2019 BASE Show details 6 Comparison of Diverse Decoding Methods from Conditional Language Models ... Ippolito, Daphne; Kriz, Reno; Kustikova, Maria. - : arXiv, 2019 BASE Show details 7 Learning translations via images with a massively multilingual image dataset Callison-Burch, Chris; Wijaya, Derry; Kriz, Reno. - : Association for Computational Linguistics, 2018 BASE Show details 8 Simplification Using Paraphrases and Context-Based Lexical Substitution Kriz, Reno; Miltsakaki, Eleni; Apidianaki, Marianna... In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-01838519 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Jun 2018, Nouvelle Orléans, United States (2018) BASE Show details

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