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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 – 6 of 6 1 First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT Muller, Benjamin; Elazar, Yanai; Sagot, Benoît... In: https://hal.inria.fr/hal-03161685 ; 2021 (2021) BASE Show details 2 First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT Muller, Benjamin; Elazar, Yanai; Sagot, Benoît... In: EACL 2021 - The 16th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03239087 ; EACL 2021 - The 16th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2021, Kyiv / Virtual, Ukraine ; https://2021.eacl.org/ (2021) BASE Show details 3 First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT ... Muller, Benjamin; Elazar, Yanai; Sagot, Benoît. - : arXiv, 2021 BASE Show details 4 Contrastive Explanations for Model Interpretability ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Choi, Yejin; Elazar, Yanai. - : Underline Science Inc., 2021 BASE Show details 5 Measuring and Improving Consistency in Pretrained Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Elazar, Yanai; Goldberg, Yoav; Hovy, Eduard; Kassner, Nora; Ravfogel, Shauli; Ravichander, Abhilasha; Schütze, Hinrich. - : Underline Science Inc., 2021 Abstract: Consistency of a model — that is, the invariance of its behavior under meaning preserving alternations in its input — is a highly desirable property in natural language processing. In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge? To this end, we create PARAREL , a high-quality resource of cloze-style query English paraphrases. It contains a total of 328 paraphrases for 38 relations. Using PARAREL , we show that the consistency of all PLMs we experiment with is poor – though with high variance between relations. Our analysis of the representational spaces of PLMs suggests that they have a poor structure and are currently not suitable for representing knowledge robustly. Finally, we propose a method for improving model consistency and experimentally demonstrate its effectiveness. ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://underline.io/lecture/38196-measuring-and-improving-consistency-in-pretrained-language-models https://dx.doi.org/10.48448/rb2w-8959 BASE Hide details 6 Amnesic Probing: Behavioral Explanation With Amnesic Counterfactuals ... 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; Elazar, Yanai; Goldberg, Yoav. - : Underline Science Inc., 2021 BASE Show details

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