Discourse structures are beneficial for various NLP tasks such as dialogue
understanding, question answering, sentiment analysis, and so on. This paper
presents a deep sequential model for parsing discourse dependency structures of
multi-party dialogues. The proposed model aims to construct a discourse
dependency tree by predicting dependency relations and constructing the
discourse structure jointly and alternately. It makes a sequential scan of the
Elementary Discourse Units (EDUs) in a dialogue. For each EDU, the model
decides to which previous EDU the current one should link and what the
corresponding relation type is. The predicted link and relation type are then
used to build the discourse structure incrementally with a structured encoder.
During link prediction and relation classification, the model utilizes not only
local information that represents the concerned EDUs, but also global
information that encodes the EDU sequence and the discourse structure that is
already built at the current step. Experiments show that the proposed model
outperforms all the state-of-the-art baselines.

本文提出了一种深度顺序模型，用于解析多方对话的话语依赖结构，并利用预测的依赖关系和结构类型共同和交替地构建话语结构，实现基于序列扫描的话语依赖树构建。实验结果表明，该模型的性能优于所有最先进的基线。