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.

翻译：检索器-阅读器框架在开放域问答（ODQA）中较为流行，其中检索器从大规模语料库中为阅读器采样一组相关的候选段落。该方法背后的关键假设是，检索器给出的高相关性得分很可能表明阅读器的高可回答性，这意味着检索到的段落包含给定问题答案的概率较高。在本工作中，我们通过经验事实否定了这一观点，并观察到基于DPR的最新密集检索模型往往将不可回答的反事实段落排在可回答的原始段落之前。为了解决密集检索器中这种对可回答性不敏感的问题，我们尝试利用反事实样本作为额外的训练资源，以更好地同步DPR的相关性度量与问题-段落对的可回答性。具体而言，我们提出了反事实枢轴对比学习（PiCL），这是一种新颖的段落检索表示学习方法，它利用反事实样本作为正样本与负样本在所学嵌入空间中的枢轴。我们将PiCL融入检索器训练中，以展示PiCL在ODQA基准测试上的有效性以及所学模型的鲁棒性。