A speaker-aware multiparty dialogue discourse parser with heterogeneous graph neural network

Discourse parsing for multiparty dialogue aims to detect the discourse structure and relations in a dialogue to obtain a discourse dependency graph. Existing models for this task have proven the effect of speaker information. However, no model explicitly learns representations of the speaker for parsing the discourse structure of dialogues. In this paper, to further exploit the effect of speaker information, we propose a novel model HG-MDP, which uses a heterogeneous graph neural network to encode dialogue graphs and we use an iterative update for the aggregation of speaker nodes and utterance nodes. Finally, we adopt updated utterance nodes to predict discourse dependency links and relations using the biaffine module. To validate our HG-MDP model, we perform experiments on the two existing benchmarks STAC and Molweni corpus. The results prove the effectiveness of the speaker modeling module on two datasets and we achieve the state-of-the-art on the Molweni dataset.