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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 – 10 of 10 1 Figurative Language in Recognizing Textual Entailment ... 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; Chakrabarty, Tuhin; Ghosh, Debanjan. - : Underline Science Inc., 2021 BASE Show details 2 Discovering changes in birthing narratives during COVID-19 ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Caitlin; Mayat, Noreen. - : Underline Science Inc., 2021 BASE Show details 3 A Survey on Recognizing Textual Entailment as an NLP Evaluation ... Poliak, Adam. - : arXiv, 2020 BASE Show details 4 Probing Neural Language Models for Human Tacit Assumptions ... Weir, Nathaniel; Poliak, Adam; Van Durme, Benjamin. - : arXiv, 2020 BASE Show details 5 What do you learn from context? Probing for sentence structure in contextualized word representations ... Tenney, Ian; Xia, Patrick; Chen, Berlin. - : arXiv, 2019 BASE Show details 6 On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference ... Belinkov, Yonatan; Poliak, Adam; Shieber, Stuart M.. - : arXiv, 2019 BASE Show details 7 On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference Belinkov, Yonatan; Poliak, Adam; Shieber, Stuart; Van Durme, Benjamin; Rush, Alexander Sasha. - 2019 Abstract: Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representa- tions free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy. ; Accepted Manuscript URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:40827358 BASE Hide details 8 Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference Belinkov, Yonatan; Poliak, Adam; Shieber, Stuart. - : Association of Computational Linguistics, 2019 BASE Show details 9 Efficient, Compositional, Order-Sensitive N-Gram Embeddings ... Poliak, Adam; Rastogi, Pushpendre; Martin, M. Patrick. - : Zenodo, 2017 BASE Show details 10 Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles ... Ferraro, Francis; Poliak, Adam; Cotterell, Ryan. - : arXiv, 2017 BASE Show details

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