The retriever-reader framework is popular for open-domain question answering
(ODQA), where a retriever samples for the reader a set of relevant candidate
passages from a large corpus. A key assumption behind this method is that high
relevance scores from the retriever likely indicate high answerability from the
reader, which implies a high probability that the retrieved passages contain
answers to a given question. In this work, we empirically dispel this belief
and observe that recent dense retrieval models based on DPR often rank
unanswerable counterfactual passages higher than their answerable original
passages. To address such answer-unawareness in dense retrievers, we seek to
use counterfactual samples as additional training resources to better
synchronize the relevance measurement of DPR with the answerability of
question-passage pairs. Specifically, we present counterfactually-Pivoting
Contrastive Learning (PiCL), a novel representation learning approach for
passage retrieval that leverages counterfactual samples as pivots between
positive and negative samples in their learned embedding space. We incorporate
PiCL into the retriever training to show the effectiveness of PiCL on ODQA
benchmarks and the robustness of the learned models.

本文介绍了一个新颖的检索方法 PiCL，它利用反事实样本来训练检索器以提高检索准确率，并在开放域问答基准测试中证明了其有效性和鲁棒性。