  In conversational search, which aims to retrieve passages containing
essential information, queries suffer from high dependency on the preceding
dialogue context. Therefore, reformulating conversational queries into
standalone forms is essential for the effective utilization of off-the-shelf
retrievers. Previous methodologies for conversational query search frequently
depend on human-annotated gold labels. However, these manually crafted queries
often result in sub-optimal retrieval performance and require high collection
costs. In response to these challenges, we propose Iterative Conversational
Query Reformulation (IterCQR), a methodology that conducts query reformulation
without relying on human oracles. IterCQR iteratively trains the QR model by
directly leveraging signal from information retrieval (IR) as a reward. Our
proposed IterCQR method shows state-of-the-art performance on two datasets,
demonstrating its effectiveness on both sparse and dense retrievers. Notably,
IterCQR exhibits robustness in domain-shift, low-resource, and topic-shift
scenarios.

Information Retrieval