Semantic Role Labeling Guided Multi-turn Dialogue ReWriter

• URL: http://arxiv.org/abs/2010.01417v1
• Date: Sat, 3 Oct 2020 19:50:04 GMT
• Title: Semantic Role Labeling Guided Multi-turn Dialogue ReWriter
• Authors: Kun Xu and Haochen Tan and Linfeng Song and Han Wu and Haisong Zhang and Linqi Song and Dong Yu
• Abstract summary: We propose to use semantic role labeling (SRL) to highlight the core semantic information of who did what to whom. Experiments show that this information significantly improves a RoBERTa-based model that already outperforms previous state-of-the-art systems.
• Score: 63.07073750355096
• License: http://creativecommons.org/licenses/by/4.0/
• 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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