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.
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.
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random negatives are often too trivial to train a reliable model
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(HCL) framework that
framework that trains the matching model in an "easy-to-difficult" scheme. Our learning framework
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a response candidate. As for IC, it
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HCL
learning framework