  Multi-party multi-turn dialogue comprehension brings unprecedented challenges
on handling the complicated scenarios from multiple speakers and criss-crossed
discourse relationship among speaker-aware utterances. Most existing methods
deal with dialogue contexts as plain texts and pay insufficient attention to
the crucial speaker-aware clues. In this work, we propose an enhanced
speaker-aware model with masking attention and heterogeneous graph networks to
comprehensively capture discourse clues from both sides of speaker property and
speaker-aware relationships. With such comprehensive speaker-aware modeling,
experimental results show that our speaker-aware model helps achieves
state-of-the-art performance on the benchmark dataset Molweni. Case analysis
shows that our model enhances the connections between utterances and their own
speakers and captures the speaker-aware discourse relations, which are critical
for dialogue modeling.
