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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 Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution ... Emelin, Denis; Sennrich, Rico. - : Association for Computational Linguistics, 2021 BASE Show details 2 Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Choi, Yejin; D. Hwang, Jena. - : Underline Science Inc., 2021 BASE Show details 3 Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Emelin, Denis; Sennrich, Rico. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.670/ Abstract: Winograd schemas are a well-established tool for evaluating coreference resolution (CoR) and commonsense reasoning (CSR) capabilities of computational models. So far, schemas remained largely confined to English, limiting their utility in multilingual settings. This work presents Wino-X, a parallel dataset of German, French, and Russian schemas, aligned with their English counterparts. We use this resource to investigate whether neural machine translation (NMT) models can perform CoR that requires commonsense knowledge and whether multilingual language models (MLLMs) are capable of CSR across multiple languages. Our findings show Wino-X to be exceptionally challenging for NMT systems that are prone to undesireable biases and unable to detect disambiguating information. We quantify biases using established statistical methods and define ways to address both of these issues. We furthermore present evidence of active cross-lingual ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing URL: https://underline.io/lecture/37769-wino-x-multilingual-winograd-schemas-for-commonsense-reasoning-and-coreference-resolution https://dx.doi.org/10.48448/cvx9-cs20 BASE Hide details 4 Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks ... Emelin, Denis; Titov, Ivan; Sennrich, Rico. - : Association for Computational Linguistics, 2020 BASE Show details 5 Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts ... Emelin, Denis; Titov, Ivan; Sennrich, Rico. - : Association for Computational Linguistics, 2019 BASE Show details

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