Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension

9 Sep 2021  ·  Xinbei Ma, Zhuosheng Zhang, Hai Zhao ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Question Answering FriendsQA Ma et al. - ELECTRA EM 58.7 # 1
F1 75.4 # 1
Question Answering Molweni Ma et al. - ELECTRA F1 72.2 # 2
EM 58.6 # 1


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