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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) BASE Show 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; Ippolito, Daphne; Callahan, Brendan; Hewitt, John. - : Association for Computational Linguistics, 2018 Abstract: We conduct the most comprehensive study to date into translating words via images. To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents. Past datasets have been limited to only a few high-resource languages and unrealistically easy translation settings. In contrast, we have collected by far the largest available dataset for this task, with images for approximately 10,000 words in each of 100 languages. We run experiments on a dozen high resource languages and 20 low resources languages, demonstrating the effect of word concreteness and part-of-speech on translation quality. %We find that while image features work best for concrete nouns, they are sometimes effective on other parts of speech. To improve image-based translation, we introduce a novel method of predicting word concreteness from images, which improves on a previous state-of-the-art unsupervised technique. This allows us to predict when image-based translation may be effective, enabling consistent improvements to a state-of-the-art text-based word translation system. Our code and the Massively Multilingual Image Dataset (MMID) are available at http://multilingual-images.org/. ; Published version Keyword: Computational linguistics; Linguistics; Massively Multilingual Image Dataset; Translations URL: https://www.aclweb.org/portal/ https://doi.org/10.18653/v1/P18-1239 https://hdl.handle.net/2144/38467 BASE Hide 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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