Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension
This work solves the problem of improving dialogue modeling for researchers and practitioners in natural language processing, though it is incremental as it builds on existing methods.
The paper tackles the challenge of multi-party multi-turn dialogue comprehension by addressing insufficient attention to speaker-aware clues, achieving state-of-the-art performance on the Molweni benchmark dataset.
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.