论文标题
发放域的生成域检索回答
Generation-Augmented Retrieval for Open-domain Question Answering
论文作者
论文摘要
我们提出了一代增强的检索（GAR）来回答开放域问题，该问题通过在没有外部资源作为监督的情况下通过文本生成启发式发现的相关环境来增加查询。我们证明，生成的上下文在很大程度上丰富了查询的语义和稀疏表示（BM25）的语义，而与最先进的密集检索方法（如DPR）相比，具有可比性或更好的性能。我们表明，为查询生成多种环境是有益的，因为融合其结果会始终获得更好的检索准确性。此外，由于稀疏和密集的表示通常是互补的，因此可以轻松地将GAR与DPR相结合，以实现更好的性能。 GAR配备了提取读取器时，在提取质量检查设置下的自然问题和Triviaqa数据集方面取得了最新的性能，并且在使用相同的生成读取器时始终优于其他检索方法。
We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.
