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Toward Deconfounding the Influence of Entity Demographics for Question
Answering Accuracy
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
- CMLFaML
Abstract
The 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, model accuracy analysis reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic). But QA's lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.
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