In conversational search (CS), it needs holistic understanding over conversational inputs to retrieve relevant passages. In this paper, we demonstrate the existence of a retrieval shortcut in CS, which causes models to retrieve passages solely relying on partial history while disregarding the latest question. With in-depth analysis, we first show naively trained dense retrievers heavily exploit the shortcut and hence perform poorly when asked to answer history-independent questions. To prevent models from solely relying on the shortcut, we explore iterative hard negatives mined by pre-trained dense retrievers. Experimental results show that training with the iterative hard negatives effectively mitigates the dependency on the shortcut and makes substantial improvement on recent CS benchmarks. Our retrievers achieve new state-of-the-art results, outperforming the previous best models by 9.7 in Recall@10 on QReCC and 12.4 in Recall@5 on TopiOCQA. Furthermore, in our end-to-end QA experiments, FiD readers combined with our retrievers surpass the previous state-of-the-art models by 3.7 and 1.0 EM scores on QReCC and TopiOCQA, respectively.

本篇论文探讨了会话式搜索中存在的检索捷径，导致模型仅依赖于模式化的历史信息进行检索，而忽略最新的问题，并提出了拉丁激烈的负面挖掘策略，以训练更加健壮的模型。经过实验证明，使用基于模型的重负面挖掘的方法有效地减缓了依赖捷径的影响，显著提高了最近的CS基准中的密集检索器的效果，尤其是在QReCC中，我们的检索器在Recall@10上的效果比以前的最先进模型高11.0。

会话式搜索中解除密集检索器的快捷依赖