Hierarchical graph fusion network and a new argumentative dataset for multiparty dialogue discourse parsing

Abstract

Discourse parsing in multi-party dialogue aims to extract the relationships between elementary discourse units (EDUs) such as arguments and utterances, and has numerous applications like chatbots or virtual assistants. Two significant challenges have been encountered in previous works: the obstacles in fusing various contexts in the modeling; and the lack of argumentative dialogue datasets in this field. To tackle context fusion challenges in the modeling, we introduce the Hierarchical Graph Fusion Network (HGFN). This method introduces sufficient contexts by hierarchically modeling the dialogue and minimizes context noise through a novel routing mechanism. It specifically: (1) Encodes multiple levels of contexts using hierarchical graph neural networks. During this stage, the router allows information exchange across different levels, expanding the model’s receptive field in dialogue. (2) Fuses matching signals from multiple levels of contexts with a fusion network. Here, the router restricts the information flows to the same context level, efficiently minimizing noise from irrelevant context. Furthermore, despite the significance of argumentative multi-party dialogue in real-world applications, this area remains largely unexplored due to dataset scarcity. To address this challenge, we develop two meticulously annotated datasets, MRDL and MRDR. Unlike the prevailing datasets that primarily focus on short and colloquial conversations, our datasets feature intricate argumentative dialogues and are publicly accessible at https://github.com/AI0Research/MRDL-and-MRDR. Our new datasets and the HGFN model could promote further advancements in this field. Extensive experiments are conducted and it is revealed that the HGFN model surpasses the state-of-the-art, particularly in complex, argumentative dialogues.