Conversational dense retrieval has shown to be effective in conversational search. However, a major limitation of conversational dense retrieval is their lack of interpretability, hindering intuitive understanding of model behaviors for targeted improvements. This paper presents CONVINV, a simple yet effective approach to shed light on interpretable conversational dense retrieval models. CONVINV transforms opaque conversational session embeddings into explicitly interpretable text while faithfully maintaining their original retrieval performance as much as possible. Such transformation is achieved by training a recently proposed Vec2Text model based on the ad-hoc query encoder, leveraging the fact that the session and query embeddings share the same space in existing conversational dense retrieval. To further enhance interpretability, we propose to incorporate external interpretable query rewrites into the transformation process. Extensive evaluations on three conversational search benchmarks demonstrate that CONVINV can yield more interpretable text and faithfully preserve original retrieval performance than baselines. Our work connects opaque session embeddings with transparent query rewriting, paving the way toward trustworthy conversational search.

翻译：会话稠密检索已被证明在会话搜索中效果显著。然而，会话稠密检索的一个主要局限在于缺乏可解释性，这阻碍了对模型行为的直观理解以实现针对性改进。本文提出CONVINV方法，这是一种简洁而有效的途径，旨在揭示可解释的会话稠密检索模型。CONVINV将不透明的会话嵌入转化为显式可解释文本，同时尽可能忠实保持其原始检索性能。该转化通过训练基于即席查询编码器的近期提出的Vec2Text模型实现，其关键在于现有会话稠密检索中会话嵌入与查询嵌入共享同一向量空间。为进一步增强可解释性，我们提出将外部可解释的查询重写融入转化过程。在三个会话搜索基准上的广泛评估表明，CONVINV相比基线方法能生成更具可解释性的文本，且更忠实地保留原始检索性能。我们的工作将不透明的会话嵌入与透明的查询重写相关联，为构建可信赖的会话搜索铺平道路。