  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
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.
