Multi-tasking Dialogue Comprehension with Discourse Parsing
This work addresses the challenge of understanding multi-party dialogues for QA systems, but it is incremental as it builds on existing discourse parsing methods.
The authors tackled multi-party dialogue machine reading comprehension by proposing a multi-task model that jointly performs question-answering and discourse parsing, showing that this approach improves both tasks, particularly for longer dialogues.
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.