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.

本论文提出一种新颖简单的数据增强策略 ——Bilateral Generation（BiG），并设计了一个对比训练目标来提高现有标记数据中排名问题答案对的性能，通过使用两个预训练生成模型，即问题生成器和答案生成器，与原始负 QA 对形成假正 QA 对来扩充数据集，以及设计对比性训练目标来学习排名问题答案对。实验结果表明，该方法通过充分利用现有标记数据显著提高了排名模型，可轻松应用于不同排名模型。