  Conversational search facilitates complex information retrieval by enabling
multi-turn interactions between users and the system. Supporting such
interactions requires a comprehensive understanding of the conversational
inputs to formulate a good search query based on historical information. In
particular, the search query should include the relevant information from the
previous conversation turns. However, current approaches for conversational
dense retrieval primarily rely on fine-tuning a pre-trained ad-hoc retriever
using the whole conversational search session, which can be lengthy and noisy.
Moreover, existing approaches are limited by the amount of manual supervision
signals in the existing datasets. To address the aforementioned issues, we
propose a History-Aware Conversational Dense Retrieval (HAConvDR) system, which
incorporates two ideas: context-denoised query reformulation and automatic
mining of supervision signals based on the actual impact of historical turns.
Experiments on two public conversational search datasets demonstrate the
improved history modeling capability of HAConvDR, in particular for long
conversations with topic shifts.
