Text retrieval using learned dense representations has recently emerged as a
promising alternative to "traditional" text retrieval using sparse bag-of-words
representations. One recent work that has garnered much attention is the dense
passage retriever (DPR) technique proposed by Karpukhin et al. (2020) for
end-to-end open-domain question answering. We present a replication study of
this work, starting with model checkpoints provided by the authors, but
otherwise from an independent implementation in our group's Pyserini IR toolkit
and PyGaggle neural text ranking library. Although our experimental results
largely verify the claims of the original paper, we arrived at two important
additional findings that contribute to a better understanding of DPR: First, it
appears that the original authors under-report the effectiveness of the BM25
baseline and hence also dense--sparse hybrid retrieval results. Second, by
incorporating evidence from the retriever and an improved answer span scoring
technique, we are able to improve end-to-end question answering effectiveness
using exactly the same models as in the original work.

本研究对 Karpukhin 等人于 2020 年提出的基于稠密编码的全域问答模型（DPR）进行了复制研究，其中发现原作者低估了 BM25 基准检索的有效性。我们通过改进的答案跨度评分技术，使用与原论文相同的模型获得更好的端到端问题答案准确性。