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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 – 7 of 7 1 Multi-Annotator Modeling to Encode Diverse Perspectives in Hate Speech Annotations ... 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; Díaz, Mark; Mostafazadeh Davani, Aida. - : Underline Science Inc., 2021 BASE Show details 2 Learning to Recognize Dialect Features ... NAACL 2021 2021; Clark, Jonathan; Demszky, Dorottya. - : Underline Science Inc., 2021 BASE Show details 3 Learning to Recognize Dialect Features ... Demszky, Dorottya; Sharma, Devyani; Clark, Jonathan H.; Prabhakaran, Vinodkumar; Eisenstein, Jacob. - : arXiv, 2020 Abstract: Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in "He {} running". In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers. For most dialects, large-scale annotated corpora for these features are unavailable, making it difficult to train recognizers. We train our models on a small number of minimal pairs, building on how linguists typically define dialect features. Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples. We also demonstrate the downstream ... : NAACL camera-ready ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2010.12707 https://dx.doi.org/10.48550/arxiv.2010.12707 BASE Hide details 4 Perturbation Sensitivity Analysis to Detect Unintended Model Biases ... Prabhakaran, Vinodkumar; Hutchinson, Ben; Mitchell, Margaret. - : arXiv, 2019 BASE Show details 5 Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions ... Prabhakaran, Vinodkumar; Ganeshkumar, Premkumar; Rambow, Owen. - : arXiv, 2018 BASE Show details 6 Statistical modality tagging from rule-based annotations and crowdsourcing ... Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : arXiv, 2015 BASE Show details 7 Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : Association for Computational Linguistics, 2012 BASE Show details

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