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    • Abdou, Mostafa (7)
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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. - : arXiv, 2020 BASE Show details 6 Do Neural Language Models Show Preferences for Syntactic Formalisms? ... Kulmizev, Artur; Ravishankar, Vinit; Abdou, Mostafa; Nivre, Joakim. - : arXiv, 2020 Abstract: Recent work on the interpretability of deep neural language models has concluded that many properties of natural language syntax are encoded in their representational spaces. However, such studies often suffer from limited scope by focusing on a single language and a single linguistic formalism. In this study, we aim to investigate the extent to which the semblance of syntactic structure captured by language models adheres to a surface-syntactic or deep syntactic style of analysis, and whether the patterns are consistent across different languages. We apply a probe for extracting directed dependency trees to BERT and ELMo models trained on 13 different languages, probing for two different syntactic annotation styles: Universal Dependencies (UD), prioritizing deep syntactic relations, and Surface-Syntactic Universal Dependencies (SUD), focusing on surface structure. We find that both models exhibit a preference for UD over SUD - with interesting variations across languages and layers - and that the strength ... : 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.2004.14096 https://arxiv.org/abs/2004.14096 BASE Hide 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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