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.
@article{arxiv.2012.14756,
title = {Dialogue Response Selection with Hierarchical Curriculum Learning},
author = {Yixuan Su and Deng Cai and Qingyu Zhou and Zibo Lin and Simon Baker and Yunbo Cao and Shuming Shi and Nigel Collier and Yan Wang},
journal= {arXiv preprint arXiv:2012.14756},
year = {2021}
}
Comments
Accepted as long paper to the main conference of ACL2021