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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. - : arXiv, 2021 BASE Show 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 Abstract: Probes, supervised models trained to predict properties (like parts-of-speech) from representations (like ELMo), have achieved high accuracy on a range of linguistic tasks. But does this mean that the representations encode linguistic structure or just that the probe has learned the linguistic task? In this paper, we propose control tasks, which associate word types with random outputs, to complement linguistic tasks. By construction, these tasks can only be learned by the probe itself. So a good probe, (one that reflects the representation), should be selective, achieving high linguistic task accuracy and low control task accuracy. The selectivity of a probe puts linguistic task accuracy in context with the probe's capacity to memorize from word types. We construct control tasks for English part-of-speech tagging and dependency edge prediction, and show that popular probes on ELMo representations are not selective. We also find that dropout, commonly used to control probe complexity, is ineffective for ... : EMNLP 2019 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1909.03368 https://arxiv.org/abs/1909.03368 BASE Hide 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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