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 .
In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers . Typically, the dual-encoder architecture is adopted to learn dense representations of questions and passages for semantic matching. However, it is difficult to effectively train a 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 hard negatives and data augmentation. The experiment results show that RocketQA significantly outperforms previous state-of-the-art models on both MSMARCO and Natural Questions. We also conduct extensive experiments to examine the effectiveness of the three strategies in RocketQA. Besides, we demonstrate that the performance of end-to-end QA can be improved based on our RocketQA retriever .
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answer finding
finding answers
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semantic
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train an effective
effectively train a
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negative sampling
hard negatives
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Extensive experiments
The experiment results
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Besides, built upon RocketQA, we achieve the first rank at the leaderboard of MSMARCO Passage Ranking Task
We also conduct extensive experiments to examine the effectiveness of the three strategies in RocketQA. Besides, we demonstrate that the performance of end-to-end QA can be improved based on our RocketQA retriever