Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialogues

• URL: http://arxiv.org/abs/2204.10558v1
• Date: Fri, 22 Apr 2022 08:15:15 GMT
• Title: Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialogues
• Authors: Gustavo Penha and Claudia Hauff
• Abstract summary: We focus on the more realistic task of full-rank retrieval of responses, where $n$ can be up to millions of responses. Our findings based on three different information-seeking dialogue datasets reveal that a learned response expansion technique is a solid baseline for sparse retrieval. We find the best performing method overall to be dense retrieval with intermediate training, followed by fine-tuning on the target conversational data.
• Score: 11.726528038065764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ranking responses for a given dialogue context is a popular benchmark in which the setup is to re-rank the ground-truth response over a limited set of $n$ responses, where $n$ is typically 10. The predominance of this setup in conversation response ranking has lead to a great deal of attention to building neural re-rankers, while the first-stage retrieval step has been overlooked. Since the correct answer is always available in the candidate list of $n$ responses, this artificial evaluation setup assumes that there is a first-stage retrieval step which is always able to rank the correct response in its top-$n$ list. In this paper we focus on the more realistic task of full-rank retrieval of responses, where $n$ can be up to millions of responses. We investigate both dialogue context and response expansion techniques for sparse retrieval, as well as zero-shot and fine-tuned dense retrieval approaches. Our findings based on three different information-seeking dialogue datasets reveal that a learned response expansion technique is a solid baseline for sparse retrieval. We find the best performing method overall to be dense retrieval with intermediate training, i.e. a step after the language model pre-training where sentence representations are learned, followed by fine-tuning on the target conversational data. We also investigate the intriguing phenomena that harder negatives sampling techniques lead to worse results for the fine-tuned dense retrieval models. The code and datasets are available at https://github.com/Guzpenha/transformer_rankers/tree/full_rank_retrieval_dialogues.

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