Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation (2021)

Abstract: In this work, we propose a novel and easy-to-apply data augmentation strategy, namely Bi lateral G eneration ( 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 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.

## introduction ranking question answer pairs, also known as answer selection, is a fundamental task in question answering (qa) systems. it aims to rank a set of candidate answers for selecting the relevant or correct answers to the given question. many efforts have been made on developing various neural models to measure the relevance degree between the question and answer pair, including siamese structure @xcite , attention-based structure @xcite @xcite , and compare-aggregate structure @xcite . recently, models with contextualized representations, e.g., elmo @xcite and bert @xcite , contribute to major improvement on answer selection @xcite . a * : katy perry has won 11 awards for her music career including three grammy awards and two american music awards. ## conclusions in this paper, we study the data augmentation for improving the performance of ranking question answer pairs when encountering data scarceness and label imbalance issues. we propose an easyto-apply and effective data augmentation strategy, namely bilateral generation, with a contrastive learning objective to maximize the utility of limited labeled data. experimental results show that the proposed method significantly improves the performance of ranking models on three factoid and non-factoid qa benchmark datasets. the proposed data augmentation strategy can be easily extended to few-shot learning scenario to further handle more severe resource-poor applications, such as domain-specific question answering, e-commerce question answering, etc.