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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 Page: 1 2 3 4 5 6 Hits 1 – 20 of 113 1 GreaseLM: Graph REASoning Enhanced Language Models for Question Answering ... Zhang, Xikun; Bosselut, Antoine; Yasunaga, Michihiro. - : arXiv, 2022 BASE Show details 2 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 5 Human-like informative conversations: Better acknowledgements using conditional mutual information ... Paranjape, Ashwin; Manning, Christopher D.. - : arXiv, 2021 BASE Show details 6 Universal Dependencies ... NAACL 2021 2021; de Marneffe, Marie-Catherine; Manning, Christopher. - : Underline Science Inc., 2021 BASE Show details 7 ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts ... Koreeda, Yuta; Manning, Christopher D.. - : arXiv, 2021 BASE Show details 8 ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; D. Manning, Christopher; Koreeda, Yuta. - : Underline Science Inc., 2021 BASE Show details 9 Conditional probing: measuring usable information beyond a baseline ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ethayarajh, Kawin; Hewitt, John. - : Underline Science Inc., 2021 BASE Show details 10 Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Fei-Fei, Li; Karamcheti, Siddharth. - : Underline Science Inc., 2021 BASE Show details 11 Human-like informative conversations: Better acknowledgements using conditional mutual information ... NAACL 2021 2021; Manning, Christopher; Paranjape, Ashwin. - : Underline Science Inc., 2021 BASE Show details 12 Biomedical and clinical English model packages for the Stanza Python NLP library Zhang, Yuhao; Zhang, Yuhui; Qi, Peng... In: J Am Med Inform Assoc (2021) BASE Show details 13 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 14 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 15 Stanza: A Python Natural Language Processing Toolkit for Many Human Languages ... Qi, Peng; Zhang, Yuhao; Zhang, Yuhui. - : arXiv, 2020 BASE Show details 16 Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ... Nivre, Joakim; de Marneffe, Marie-Catherine; Ginter, Filip. - : arXiv, 2020 BASE Show details 17 Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation ... Dhole, Kaustubh D.; Manning, Christopher D.. - : arXiv, 2020 Abstract: Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules. A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves ... : Some of the results in the paper were incorrect ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2004.08694 https://dx.doi.org/10.48550/arxiv.2004.08694 BASE Hide details 18 Finding Universal Grammatical Relations in Multilingual BERT ... Chi, Ethan A.; Hewitt, John; Manning, Christopher D.. - : arXiv, 2020 BASE Show details 19 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 20 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details Page: 1 2 3 4 5 6

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