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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 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. - : Underline Science Inc., 2021 BASE Show 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 Abstract: We find that the requirement of model interpretations to be faithful is vague and incomplete. With interpretation by textual highlights as a case-study, we present several failure cases. Borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and the attribution of human behavior to the interpretation (social attribution). We re-formulate faithfulness as an accurate attribution of causality to the model, and introduce the concept of aligned faithfulness: faithful causal chains that are aligned with their expected social behavior. The two steps of causal attribution and social attribution together complete the process of explaining behavior. With this formalization, we characterize various failures of misaligned faithful highlight interpretations, and propose an alternative causal chain to remedy the issues. Finally, we implement highlight explanations of the proposed causal format using contrastive explanations. ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://dx.doi.org/10.48448/fgdk-w171 https://underline.io/lecture/38188-aligning-faithful-interpretations-with-their-social-attribution BASE Hide details 5 Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Goldberg, Yoav; Merrill, William. - : Underline Science Inc., 2021 BASE Show details 6 Asking It All: Generating Contextualized Questions for any Semantic Role ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Dagan, Ido; Goldberg, Yoav. - : Underline Science Inc., 2021 BASE Show details 7 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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