Enhanced Speaker-Aware Multi-Party Multi-Turn Dialogue Comprehension

Multi-party multi-turn dialogue comprehension brings unprecedented challenges in handling complicated scenarios, as the co-occurrence of multiple speakers causes complexity and inconsistency. As a result of the multiple participation, the shift of speaker roles and crisscrossed discourse relations among utterances hinder reading comprehension. Motivated by this, we further integrate the enhancements of speaker-related features for dialogue comprehension performance. This work proposes a novel model with enhancement from both sides of speaker roles and speaker-aware relations. At the token level, we apply a speaker mask for attention, while at the discourse level, we utilize heterogeneous graph networks for comprehensive speaker-aware discourse clues. Experimental results show that our
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nhanced
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peaker-
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ware method (ESA) helps achieve state-of-the-art performance on the Molweni dataset, as well as significant improvements on the FriendsQA dataset. We find that our method makes steady improvements on stronger backbones. Analysis shows that our model enhances the connections between utterances and their own speakers and captures the speaker-aware discourse relations. Discussions on data features and error cases are presented, and a visualized case is displayed. The findings reveal the importance of speaker-aware signals in dialogue comprehension.