  We tackle Multi-party Dialogue Reading Comprehension (abbr., MDRC). MDRC
stands for an extractive reading comprehension task grounded on a batch of
dialogues among multiple interlocutors. It is challenging due to the
requirement of understanding cross-utterance contexts and relationships in a
multi-turn multi-party conversation. Previous studies have made great efforts
on the utterance profiling of a single interlocutor and graph-based interaction
modeling. The corresponding solutions contribute to the answer-oriented
reasoning on a series of well-organized and thread-aware conversational
contexts. However, the current MDRC models still suffer from two bottlenecks.
On the one hand, a pronoun like "it" most probably produces multi-skip
reasoning throughout the utterances of different interlocutors. On the other
hand, an MDRC encoder is potentially puzzled by fuzzy features, i.e., the
mixture of inner linguistic features in utterances and external interactive
features among utterances. To overcome the bottlenecks, we propose a
coreference-aware attention modeling method to strengthen the reasoning
ability. In addition, we construct a two-channel encoding network. It
separately encodes utterance profiles and interactive relationships, so as to
relieve the confusion among heterogeneous features. We experiment on the
benchmark corpora Molweni and FriendsQA. Experimental results demonstrate that
our approach yields substantial improvements on both corpora, compared to the
fine-tuned BERT and ELECTRA baselines. The maximum performance gain is about
2.5\% F1-score. Besides, our MDRC models outperform the state-of-the-art in
most cases.
