@inproceedings{tao-etal-2023-core,
title = "{CORE}: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection",
author = "Tao, Chongyang and
Feng, Jiazhan and
Shen, Tao and
Liu, Chang and
Li, Juntao and
Geng, Xiubo and
Jiang, Daxin",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.174",
doi = "10.18653/v1/2023.acl-long.174",
pages = "3102--3114",
abstract = "Establishing retrieval-based dialogue systems that can select appropriate responses from the pre-built index has gained increasing attention. Recent common practice is to construct a two-stage pipeline with a fast retriever (e.g., bi-encoder) for first-stage recall followed by a smart response reranker (e.g., cross-encoder) for precise ranking. However, existing studies either optimize the retriever and reranker in independent ways, or distill the knowledge from a pre-trained reranker into the retriever in an asynchronous way, leading to sub-optimal performance of both modules. Thus, an open question remains about how to train them for a better combination of the best of both worlds. To this end, we present a cooperative training of the response retriever and the reranker whose parameters are dynamically optimized by the ground-truth labels as well as list-wise supervision signals from each other. As a result, the two modules can learn from each other and evolve together throughout the training. Experimental results on two benchmarks demonstrate the superiority of our method.",
}
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%0 Conference Proceedings
%T CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection
%A Tao, Chongyang
%A Feng, Jiazhan
%A Shen, Tao
%A Liu, Chang
%A Li, Juntao
%A Geng, Xiubo
%A Jiang, Daxin
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F tao-etal-2023-core
%X Establishing retrieval-based dialogue systems that can select appropriate responses from the pre-built index has gained increasing attention. Recent common practice is to construct a two-stage pipeline with a fast retriever (e.g., bi-encoder) for first-stage recall followed by a smart response reranker (e.g., cross-encoder) for precise ranking. However, existing studies either optimize the retriever and reranker in independent ways, or distill the knowledge from a pre-trained reranker into the retriever in an asynchronous way, leading to sub-optimal performance of both modules. Thus, an open question remains about how to train them for a better combination of the best of both worlds. To this end, we present a cooperative training of the response retriever and the reranker whose parameters are dynamically optimized by the ground-truth labels as well as list-wise supervision signals from each other. As a result, the two modules can learn from each other and evolve together throughout the training. Experimental results on two benchmarks demonstrate the superiority of our method.
%R 10.18653/v1/2023.acl-long.174
%U https://aclanthology.org/2023.acl-long.174
%U https://doi.org/10.18653/v1/2023.acl-long.174
%P 3102-3114
Markdown (Informal)
[CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection](https://aclanthology.org/2023.acl-long.174) (Tao et al., ACL 2023)
ACL