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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 – 3 of 3 1 Spurious Correlations in Cross-Topic Argument Mining ... 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; Barrett, Maria; Søgaard, Anders. - : Underline Science Inc., 2021 BASE Show details 2 Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias ... Gonzalez, Ana Valeria; Barrett, Maria; Hvingelby, Rasmus; Webster, Kellie; Søgaard, Anders. - : arXiv, 2020 Abstract: The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highlight model preferences that are "hallucinatory", e.g., disambiguating gender-ambiguous occurrences of 'doctor' as male doctors. We show that for languages with type B reflexivization, e.g., Swedish and Russian, we can construct multi-task challenge datasets for detecting gender bias that lead to unambiguously wrong model predictions: In these languages, the direct translation of 'the doctor removed his mask' is not ambiguous between a coreferential reading and a disjoint reading. Instead, the coreferential reading requires a non-gendered pronoun, and the gendered, possessive pronouns are anti-reflexive. We present a multilingual, multi-task challenge dataset, which spans four languages and four NLP tasks and focuses only on this phenomenon. We find evidence for gender bias across all task-language combinations and correlate model ... : To appear in EMNLP 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2009.11982 https://arxiv.org/abs/2009.11982 BASE Hide details 3 The Sensitivity of Language Models and Humans to Winograd Schema Perturbations ... Abdou, Mostafa; Ravishankar, Vinit; Barrett, Maria. - : arXiv, 2020 BASE Show details

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