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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 – 4 of 4 1 Do Context-Aware Translation Models Pay the Right Attention? ... Yin, Kayo; Fernandes, Patrick; Pruthi, Danish. - : arXiv, 2021 BASE Show details 2 When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection ... Chaudhary, Aditi; Yin, Kayo; Anastasopoulos, Antonios. - : arXiv, 2021 BASE Show details 3 When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi. - : Underline Science Inc., 2021 BASE Show details 4 Do Context-Aware Translation Models Pay the Right Attention? ... 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; Chaudhary, Aditi; Fernandes, Patrick; F. T. Martins, André; Neubig, Graham; Pruthi, Danish; Yin, Kayo. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.65 Abstract: Context-aware machine translation models are designed to leverage contextual information, but often fail to do so. As a result, they inaccurately disambiguate pronouns and polysemous words that require context for resolution. In this paper, we ask several questions: What contexts do human translators use to resolve ambiguous words? Are models paying large amounts of attention to the same context? What if we explicitly train them to do so? To answer these questions, we introduce SCAT (Supporting Context for Ambiguous Translations), a new English-French dataset comprising supporting context words for 14K translations that professional translators found useful for pronoun disambiguation. Using SCAT, we perform an in-depth analysis of the context used to disambiguate, examining positional and lexical characteristics of the supporting words. Furthermore, we measure the degree of alignment between the model's attention scores and the supporting ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/25426-do-context-aware-translation-models-pay-the-right-attentionquestion https://dx.doi.org/10.48448/20nd-s715 BASE Hide details

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