论文标题
语义角色标签指导的多扭对话重写者
Semantic Role Labeling Guided Multi-turn Dialogue ReWriter
论文作者
论文摘要
对于多转对话的重写，有效地建模对话环境中语言知识并摆脱噪音的能力对于提高其性能至关重要。现有的细心模型会注意所有单词，而没有事先重点，这导致对某些可用单词的注意力不准确。在本文中，我们建议使用语义角色标签（SRL），该标签（SRL）强调了谁对谁做了谁，为重写器模型提供了其他指导。实验表明，这些信息显着改善了基于罗伯塔的模型，该模型已经超过了先前的最先进的系统。
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.
