2021
EMNLP
EMNLP 2021
Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy
Abstract
AbstractThe goal of question answering (QA) is to answer _any_ question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, an analysis of model accuracy reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic) and question ambiguity. But QA’s lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.
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Keyword Pioneer
— entity demographics
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio