CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation

• URL: http://arxiv.org/abs/2309.06748v1
• Date: Wed, 13 Sep 2023 06:40:24 GMT
• Title: CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation
• Authors: Chao-Wei Huang, Chen-Yu Hsu, Tsu-Yuan Hsu, Chen-An Li, Yun-Nung Chen
• Abstract summary: We propose CONVERSER, a framework for training conversational dense retrievers with at most 6 examples of in-domain dialogues. 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 CONVERSER achieves comparable performance to fully-supervised models.
• Score: 32.10366004426449
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 CONVERSER achieves comparable performance to fully-supervised models, demonstrating the effectiveness of our proposed framework in few-shot conversational dense retrieval. All source code and generated datasets are available at https://github.com/MiuLab/CONVERSER

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