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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 – 5 of 5 1 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 2 Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Goldberg, Yoav; Merrill, William. - : Underline Science Inc., 2021 BASE Show details 3 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 4 Aligning Faithful Interpretations with their Social Attribution ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Goldberg, Yoav; Jacovi, Alon. - : Underline Science Inc., 2021 BASE Show details 5 Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Goldberg, Yoav; Linzen, Tal. - : Underline Science Inc., 2021 BASE Show details

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