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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 Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ... Zhao, Jieyu; Khashabi, Daniel; Khot, Tushar. - : arXiv, 2021 BASE Show details 2 Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ... 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; Chang, Kai-Wei; Khashabi, Daniel. - : Underline Science Inc., 2021 BASE Show details 3 "The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition ... Zhou, Yichao; Jiang, Jyun-Yu; Zhao, Jieyu. - : arXiv, 2020 BASE Show details 4 Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer ... Zhao, Jieyu; Mukherjee, Subhabrata; Hosseini, Saghar. - : arXiv, 2020 BASE Show details 5 Examining Gender Bias in Languages with Grammatical Gender ... Zhou, Pei; Shi, Weijia; Zhao, Jieyu; Huang, Kuan-Hao; Chen, Muhao; Cotterell, Ryan; Chang, Kai-Wei. - : arXiv, 2019 Abstract: Recent studies have shown that word embeddings exhibit gender bias inherited from the training corpora. However, most studies to date have focused on quantifying and mitigating such bias only in English. These analyses cannot be directly extended to languages that exhibit morphological agreement on gender, such as Spanish and French. In this paper, we propose new metrics for evaluating gender bias in word embeddings of these languages and further demonstrate evidence of gender bias in bilingual embeddings which align these languages with English. Finally, we extend an existing approach to mitigate gender bias in word embeddings under both monolingual and bilingual settings. Experiments on modified Word Embedding Association Test, word similarity, word translation, and word pair translation tasks show that the proposed approaches effectively reduce the gender bias while preserving the utility of the embeddings. ... : 9 pages, 4 figures. Accepted at EMNLP-IJCNLP 2019 as a long paper ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1909.02224 https://arxiv.org/abs/1909.02224 BASE Hide details

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