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- W3115777319 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." @default.
- W3115777319 created "2021-01-05" @default.
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- W3115777319 date "2020-12-29" @default.
- W3115777319 modified "2023-09-26" @default.
- W3115777319 title "Dialogue Response Selection with Hierarchical Curriculum Learning" @default.
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- W3115777319 doi "https://doi.org/10.48550/arxiv.2012.14756" @default.
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