TGAT-DGL: Triple Graph Attention Networks on Dual-Granularity Level for Multi-party Dialogue Reading Comprehension

Multi-party dialogue reading comprehension is an extraction-based reading comprehension task that aims to understand dialogue with multiple interlocutors and answer related questions. The frequent rotation of topics and the irregular order of interlocutors in dialogues may lead to the scattered distribution of information in multi-party dialogues. This means that the model needs to effectively integrate information across multiple utterances and among various interlocutors. Although previous methods have made considerable efforts in mining and modeling dialogue-related features, they still encounter two key issues. On the one hand, these methods failed to solve the cross-utterance co-reference problem that arises from the coexistence of multiple topics and interlocutors in the dialogue. On the other hand, they mostly ignored the joint reasoning of multi-granularity dialogue-related features, which can parse the semantic space of multi-party dialogue from coarse to fine. To overcome these bottlenecks, we propose a dual-granularity information joint reasoning method, which performs hierarchically semantic modeling for multi-party dialogue based on the graph attention networks. Specifically, we utilize discourse dependency relationships and interlocutor-aware temporal information to conduct coarsegrained semantic modeling, and perform fine-grained semantic refinement by leveraging token-level co-reference relationships. Our method demonstrates stable and substantial performance improvement when using different pre-trained language models as backbones and achieves a new state-of-the-art on the benchmark corpora Molweni and FriendsQA.