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.

翻译：我们研究多方对话阅读理解（简称MDRC）。MDRC是一项基于多个对话者之间对话批次的抽取式阅读理解任务。由于需要理解跨语句上下文以及多方多轮对话中的关系，该任务具有挑战性。以往研究在单个对话者的语句刻画和基于图的交互建模方面付出了巨大努力。这些解决方案有助于在一系列组织良好且具有线索感知的对话上下文上进行面向答案的推理。然而，当前MDRC模型仍面临两个瓶颈。一方面，像“it”这样的代词很可能在不同对话者的语句中产生多跳推理。另一方面，MDRC编码器可能被模糊特征所困惑，即语句内部语言特征与语句之间外部交互特征的混合。为克服这些瓶颈，我们提出了一种核心指代感知的注意力建模方法以增强推理能力。此外，我们构建了一个双通道编码网络，分别对语句刻画和交互关系进行编码，从而缓解异质特征之间的混淆。我们在基准语料库Molweni和FriendsQA上进行了实验。实验结果表明，与微调后的BERT和ELECTRA基线相比，我们的方法在两个语料库上均取得了显著改进，性能最高提升约2.5%的F1分数。此外，我们的MDRC模型在大多数情况下优于当前最先进方法。