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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 – 7 of 7 1 Challenges and Strategies in Cross-Cultural NLP ... Hershcovich, Daniel; Frank, Stella; Lent, Heather. - : arXiv, 2022 BASE Show details 2 Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Abdou, Mostafa; Frank, Stella. - : Underline Science Inc., 2021 BASE Show details 3 Attention Can Reflect Syntactic Structure (If You Let It) ... Ravishankar, Vinit; Kulmizev, Artur; Abdou, Mostafa. - : arXiv, 2021 BASE Show details 4 Does injecting linguistic structure into language models lead to better alignment with brain recordings? ... Abdou, Mostafa; Gonzalez, Ana Valeria; Toneva, Mariya. - : arXiv, 2021 BASE Show details 5 The Sensitivity of Language Models and Humans to Winograd Schema Perturbations ... Abdou, Mostafa; Ravishankar, Vinit; Barrett, Maria; Belinkov, Yonatan; Elliott, Desmond; Søgaard, Anders. - : arXiv, 2020 Abstract: Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues. ... : ACL 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2005.01348 https://arxiv.org/abs/2005.01348 BASE Hide details 6 Do Neural Language Models Show Preferences for Syntactic Formalisms? ... Kulmizev, Artur; Ravishankar, Vinit; Abdou, Mostafa. - : arXiv, 2020 BASE Show details 7 X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension ... Abdou, Mostafa; Sas, Cezar; Aralikatte, Rahul. - : arXiv, 2019 BASE Show details

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