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Semantic Role Labeling Guided Multi-turn Dialogue ReWriter

3 October 2020
Kun Xu
Haochen Tan
Linfeng Song
Han Wu
Haisong Zhang
Linqi Song
Dong Yu
    KELM
    OffRL
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Abstract

For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting rid of the noises is essential to improve its performance. Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words. In this paper, we propose to use semantic role labeling (SRL), which highlights the core semantic information of who did what to whom, to provide additional guidance for the rewriter model. Experiments show that this information significantly improves a RoBERTa-based model that already outperforms previous state-of-the-art systems.

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