  Multi-party dialogue machine reading comprehension (MRC) brings tremendous
challenge since it involves multiple speakers at one dialogue, resulting in
intricate speaker information flows and noisy dialogue contexts. To alleviate
such difficulties, previous models focus on how to incorporate these
information using complex graph-based modules and additional manually labeled
data, which is usually rare in real scenarios. In this paper, we design two
labour-free self- and pseudo-self-supervised prediction tasks on speaker and
key-utterance to implicitly model the speaker information flows, and capture
salient clues in a long dialogue. Experimental results on two benchmark
datasets have justified the effectiveness of our method over competitive
baselines and current state-of-the-art models.
