Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019). However, current phrase retrieval models heavily depend on their sparse representations while still underperforming retriever-reader approaches. In this work, we show for the first time that we can learn dense phrase representations alone that achieve much stronger performance in open-domain QA. Our approach includes (1) learning query-agnostic phrase representations via question generation and distillation; (2) novel negative-sampling methods for global normalization; (3) query-side fine-tuning for transfer learning. On five popular QA datasets, our model DensePhrases improves previous phrase retrieval models by 15%-25% absolute accuracy and matches the performance of state-of-the-art retriever-reader models. Our model is easy to parallelize due to pure dense representations and processes more than 10 questions per second on CPUs. Finally, we directly use our pre-indexed dense phrase representations for two slot filling tasks, showing the promise of utilizing DensePhrases as a dense knowledge base for downstream tasks.

该研究展示了从阅读理解任务的监督学习中学习到短语的密集表示，并使用负采样方法提高性能，能够取代当前依赖于稀疏表示的短语检索模型，实现更好的开放域问答准确性并被用作密集知识库。

大规模学习短语的密集表示