Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation

• URL: http://arxiv.org/abs/2106.11096v1
• Date: Mon, 21 Jun 2021 13:29:43 GMT
• Title: Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation
• Authors: Yang Deng, Wenxuan Zhang, Wai Lam
• Abstract summary: We propose a novel and easy-to-apply data augmentation strategy, namely Bilateral Generation (BiG) With the augmented dataset, we design a contrastive training objective for learning to rank question answer pairs. Experimental results on three benchmark datasets, namely TREC-QA, WikiQA, and ANTIQUE, show that our method significantly improves the performance of ranking models.
• Score: 39.22166065525888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a novel and easy-to-apply data augmentation strategy, namely Bilateral Generation (BiG), with a contrastive training objective for improving the performance of ranking question answer pairs with existing labeled data. In specific, we synthesize pseudo-positive QA pairs in contrast to the original negative QA pairs with two pre-trained generation models, one for question generation, the other for answer generation, which are fine-tuned on the limited positive QA pairs from the original dataset. With the augmented dataset, we design a contrastive training objective for learning to rank question answer pairs. Experimental results on three benchmark datasets, namely TREC-QA, WikiQA, and ANTIQUE, show that our method significantly improves the performance of ranking models by making full use of existing labeled data and can be easily applied to different ranking models.

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