  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.
