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arXiv · 2104.08735

Learning with Instance Bundles for Reading Comprehension

Dheeru Dua·Pradeep Dasigi·Sameer Singh·Matt Gardner
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Abstract

When training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other. However, closely related questions and their corresponding answers are not independent, and leveraging these relationships could provide a strong supervision signal to a model. Drawing on ideas from contrastive estimation, we introduce several new supervision techniques that compare question-answer scores across multiple related instances. Specifically, we normalize these scores across various neighborhoods of closely contrasting questions and/or answers, adding another cross entropy loss term that is used in addition to traditional maximum likelihood estimation. Our techniques require bundles of related question-answer pairs, which we can either mine from within existing data or create using various automated heuristics. We empirically demonstrate the effectiveness of training with instance bundles on two datasets -- HotpotQA and ROPES -- showing up to 11% absolute gains in accuracy.

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Dheeru Dua, Pradeep Dasigi, Sameer Singh, Matt Gardner. 2021-04-18. Learning with Instance Bundles for Reading Comprehension. https://arxiv.org/abs/2104.08735

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