In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for answer finding. Typically, the dual-encoder architecture is adopted to learn dense representations of questions and passages for matching. However, it is difficult to train an effective dual-encoder due to the challenges including the discrepancy between training and inference, the existence of unlabeled positives and limited training data. To address these challenges, we propose an optimized training approach, called RocketQA, to improving dense passage retrieval. We make three major technical contributions in RocketQA, namely cross-batch negatives, denoised negative sampling and data augmentation. Extensive experiments show that RocketQA significantly outperforms previous state-of-the-art models on both MSMARCO and Natural Questions. Besides, built upon RocketQA, we achieve the first rank at the leaderboard of MSMARCO Passage Ranking Task.

本文提出了一种优化的训练方法RocketQA来改进密集问答检索，通过跨批次负样例、去噪硬负样例和数据增强等三种策略的实验，证明该方法在MSMARCO和Natural Questions上都显著优于之前的最先进模型，并提高了end-to-end QA的性能。

RocketQA: 开放领域问答中基于密集文本检索的优化训练方法