Related papers: Dialogue Response Selection with Hierarchical Curriculum Learning

Dialogue Response Selection with Hierarchical Curriculum Learning

URL: http://arxiv.org/abs/2012.14756v1
Date: Tue, 29 Dec 2020 14:06:41 GMT
Title: Dialogue Response Selection with Hierarchical Curriculum Learning
Authors: Yixuan Su, Deng Cai, Qingyu Zhou, Zibo Lin, Simon Baker, Yunbo Cao, Shuming Shi, Nigel Collier, Yan Wang
Abstract summary: We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that random negatives are often too trivial to train a reliable model, we propose a hierarchical curriculum learning framework.
Score: 52.3318584971562
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that random negatives are often too trivial to train a reliable model, we propose a hierarchical curriculum learning (HCL) framework that 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 response. On the other hand, IC progressively strengthens the model's ability in identifying the mismatched information between the dialogue context and response. Empirical studies on two benchmark datasets with three state-of-the-art matching models demonstrate that the proposed HCL significantly improves the model performance across various evaluation metrics.

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