Contrastive Distant Supervision for Debiased and Denoised Machine Reading Comprehension
Ning Bian,
Hongyu Lin,
Xianpei Han
et al.
Abstract:Distant Supervision (DS) is a promising learning approach for MRC by leveraging easilyobtained question-answer pairs. Unfortunately, the heuristically annotated dataset will inevitably lead to mislabeled instances, resulting in answer bias and context noise problems. To learn debiased and denoised MRC models, this paper proposes the Contrastive Distant Supervision algorithm -CDS, which can learn to distinguish confusing and noisy instances via confidence-aware contrastive learning. Specifically, to eliminate a… Show more
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