  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.
