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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 – 16 of 16 1 GreaseLM: Graph REASoning Enhanced Language Models for Question Answering ... Zhang, Xikun; Bosselut, Antoine; Yasunaga, Michihiro. - : arXiv, 2022 BASE Show details 2 Prefix-Tuning: Optimizing Continuous Prompts for Generation ... 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; Li, Xiang; Liang, Percy. - : Underline Science Inc., 2021 BASE Show details 3 Green NLP panel ... 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; ., Iryna; Arase, Yuki. - : Underline Science Inc., 2021 BASE Show details 4 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 5 Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality ... Lee, Mina; Donahue, Chris; Jia, Robin; Iyabor, Alexander; Liang, Percy. - : arXiv, 2021 Abstract: We release a new benchmark for lexical substitution, the task of finding appropriate substitutes for a target word in a context. To assist humans with writing, lexical substitution systems can suggest words that humans cannot easily think of. However, existing benchmarks depend on human recall as the only source of data, and therefore lack coverage of the substitutes that would be most helpful to humans. Furthermore, annotators often provide substitutes of low quality, which are not actually appropriate in the given context. We collect higher-coverage and higher-quality data by framing lexical substitution as a classification problem, guided by the intuition that it is easier for humans to judge the appropriateness of candidate substitutes than conjure them from memory. To this end, we use a context-free thesaurus to produce candidates and rely on human judgement to determine contextual appropriateness. Compared to the previous largest benchmark, our Swords benchmark has 4.1x more substitutes per target word ... : Published as a conference paper at NAACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2106.04102 https://arxiv.org/abs/2106.04102 BASE Hide details 6 Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality ... NAACL 2021 2021; Donahue, Chris; Iyabor, Alexander. - : Underline Science Inc., 2021 BASE Show details 7 Designing and Interpreting Probes with Control Tasks ... Hewitt, John; Liang, Percy. - : arXiv, 2019 BASE Show details 8 Unanimous Prediction for 100\% Precision with Application to Learning Semantic Mappings Khani, Fereshte; Rinard, Martin; Liang, Percy In: arXiv (2019) BASE Show details 9 Generating Sentences by Editing Prototypes ... Guu, Kelvin; Hashimoto, Tatsunori B.; Oren, Yonatan. - : arXiv, 2017 BASE Show details 10 From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood ... Guu, Kelvin; Pasupat, Panupong; Liu, Evan Zheran. - : arXiv, 2017 BASE Show details 11 Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings ... He, He; Balakrishnan, Anusha; Eric, Mihail. - : arXiv, 2017 BASE Show details 12 Inferring Logical Forms From Denotations ... Pasupat, Panupong; Liang, Percy. - : arXiv, 2016 BASE Show details 13 Learning Executable Semantic Parsers for Natural Language Understanding ... Liang, Percy. - : arXiv, 2016 BASE Show details 14 Learning Dependency-Based Compositional Semantics Liang, Percy. - : eScholarship, University of California, 2011 BASE Show details 15 Learning Dependency-Based Compositional Semantics Liang, Percy. - : eScholarship, University of California, 2011 In: Liang, Percy. (2011). Learning Dependency-Based Compositional Semantics. UC Berkeley: Computer Science. Retrieved from: http://www.escholarship.org/uc/item/1b1189cm (2011) BASE Show details 16 Learning Dependency-Based Compositional Semantics ... Liang, Percy; Jordan, Michael I.; Klein, Dan. - : arXiv, 2011 BASE Show details

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