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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; Chang, Kai-Wei; Awadallah, Ahmed Hassan. - : arXiv, 2020 Abstract: Multilingual representations embed words from many languages into a single semantic space such that words with similar meanings are close to each other regardless of the language. These embeddings have been widely used in various settings, such as cross-lingual transfer, where a natural language processing (NLP) model trained on one language is deployed to another language. While the cross-lingual transfer techniques are powerful, they carry gender bias from the source to target languages. In this paper, we study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations from both the intrinsic and extrinsic perspectives. Experimental results show that the magnitude of bias in the multilingual representations changes differently when we align the embeddings to different target spaces and that the alignment direction can also have an influence ... Keyword: Computation and Language cs.CL; Computers and Society cs.CY; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2005.00699 https://arxiv.org/abs/2005.00699 BASE Hide details 5 Examining Gender Bias in Languages with Grammatical Gender ... Zhou, Pei; Shi, Weijia; Zhao, Jieyu. - : arXiv, 2019 BASE Show details

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