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Multi-Annotator Modeling to Encode Diverse Perspectives in Hate Speech Annotations ...
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Learning to Recognize Dialect Features ...
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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 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2010.12707 https://dx.doi.org/10.48550/arxiv.2010.12707
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Perturbation Sensitivity Analysis to Detect Unintended Model Biases ...
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Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions ...
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Statistical modality tagging from rule-based annotations and crowdsourcing ...
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Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing
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