Multi-tasking Dialogue Comprehension with Discourse Parsing

PACLIC 2021  ·  Yuchen He, Zhuosheng Zhang, Hai Zhao ·

Multi-party dialogue machine reading comprehension (MRC) raises an even more challenging understanding goal on dialogue with more than two involved speakers, compared with the traditional plain passage style MRC. To accurately perform the question-answering (QA) task according to such multi-party dialogue, models have to handle fundamentally different discourse relationships from common non-dialogue plain text, where discourse relations are supposed to connect two far apart utterances in a linguistics-motivated way.To further explore the role of such unusual discourse structure on the correlated QA task in terms of MRC, we propose the first multi-task model for jointly performing QA and discourse parsing (DP) on the multi-party dialogue MRC task. Our proposed model is evaluated on the latest benchmark Molweni, whose results indicate that training with complementary tasks indeed benefits not only QA task, but also DP task itself. We further find that the joint model is distinctly stronger when handling longer dialogues which again verifies the necessity of DP in the related MRC.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Discourse Parsing Molweni DP Link F1 86.6 # 1
Link & Rel F1 64.9 # 1


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