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.Comment: Accepted to AAAI 201
