@inproceedings{su-etal-2021-dialogue,
title = "Dialogue Response Selection with Hierarchical Curriculum Learning",
author = "Su, Yixuan and
Cai, Deng and
Zhou, Qingyu and
Lin, Zibo and
Baker, Simon and
Cao, Yunbo and
Shi, Shuming and
Collier, Nigel and
Wang, Yan",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.137/",
doi = "10.18653/v1/2021.acl-long.137",
pages = "1740--1751",
abstract = "We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an {\textquotedblleft}easy-to-difficult{\textquotedblright} scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, it progressively strengthens the model`s ability in identifying the mismatching information between the dialogue context and a response candidate. Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics."
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<abstract>We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an “easy-to-difficult” scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, it progressively strengthens the model‘s ability in identifying the mismatching information between the dialogue context and a response candidate. Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.</abstract>
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%0 Conference Proceedings
%T Dialogue Response Selection with Hierarchical Curriculum Learning
%A Su, Yixuan
%A Cai, Deng
%A Zhou, Qingyu
%A Lin, Zibo
%A Baker, Simon
%A Cao, Yunbo
%A Shi, Shuming
%A Collier, Nigel
%A Wang, Yan
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F su-etal-2021-dialogue
%X We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an “easy-to-difficult” scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, it progressively strengthens the model‘s ability in identifying the mismatching information between the dialogue context and a response candidate. Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.
%R 10.18653/v1/2021.acl-long.137
%U https://aclanthology.org/2021.acl-long.137/
%U https://doi.org/10.18653/v1/2021.acl-long.137
%P 1740-1751
Markdown (Informal)
[Dialogue Response Selection with Hierarchical Curriculum Learning](https://aclanthology.org/2021.acl-long.137/) (Su et al., ACL-IJCNLP 2021)
ACL
- Yixuan Su, Deng Cai, Qingyu Zhou, Zibo Lin, Simon Baker, Yunbo Cao, Shuming Shi, Nigel Collier, and Yan Wang. 2021. Dialogue Response Selection with Hierarchical Curriculum Learning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1740–1751, Online. Association for Computational Linguistics.