CONVERSER: Few-shot Conversational Dense Retrieval with Synthetic Data Generation
Chao-Wei Huang, Chen-Yu Hsu, Tsu-Yuan Hsu, Chen-An Li, Yun-Nung Chen · 2023
Conversational search provides a natural interface for information retrieval (IR).Recent approaches have demonstrated promising results in applying dense retrieval to conversational IR.However, training dense retrievers requires large amounts of in-domain paired data.This hinders the development of conversational dense retrievers, as abundant in-domain conversations are expensive to collect.In this paper, we propose CONVERSER, a framework for training conversational dense retrievers with at most 6 examples of in-domain dialogues.Specifically, we utilize the in-context learning capability of large language models to generate conversational queries given a passage in the retrieval corpus.Experimental results on conversational retrieval benchmarks OR-QuAC and TREC CAsT 19 show that the proposed CON-VERSER achieves comparable performance to fully-supervised models, demonstrating the effectiveness of our proposed framework in fewshot conversational dense retrieval.1