Tangled multi-party dialogue contexts lead to challenges for dialogue reading
comprehension, where multiple dialogue threads flow simultaneously within a
common dialogue record, increasing difficulties in understanding the dialogue
history for both human and machine. Previous studies mainly focus on utterance
encoding methods with carefully designed features but pay inadequate attention
to characteristic features of the structure of dialogues. We specially take
structure factors into account and design a novel model for dialogue
disentangling. Based on the fact that dialogues are constructed on successive
participation and interactions between speakers, we model structural
information of dialogues in two aspects: 1)speaker property that indicates whom
a message is from, and 2) reference dependency that shows whom a message may
refer to. The proposed method achieves new state-of-the-art on the Ubuntu IRC
benchmark dataset and contributes to dialogue-related comprehension.

本文针对多方对话中的混杂对话结构特点，提出使用讲话人属性和参考依赖两方面的结构信息来解决对话概要理解问题，并在 Ubuntu IRC 测试数据集上取得最新的最佳效果。