GLGR : Question-aware Global-to-Local Graph Reasoning for Multi-party Dialogue Reading Comprehension (2023)

Abstract: Graph reasoning contributes to the integration of discretely-distributed attentive information (clues) for Multi-party Dialogue Reading Comprehension (MDRC). This is attributed primarily to multi-hop reasoning over global conversational structures. However, existing approaches barely apply questions for anti-noise graph reasoning. More seriously, the local semantic structures in utterances are neglected, although they are beneficial for bridging across semantically-related clues. In this paper, we propose a question-aware global-to-local graph reasoning approach. It expands the canonical Interlocutor-Utterance graph by introducing a question node, enabling comprehensive global graph reasoning. More importantly, it constructs a semantic-role graph for each utterance, and accordingly performs local graph reasoning conditioned on the semantic relations. We design a two-stage encoder network to implement the progressive reasoning from the global graph to local. The experiments on the benchmark datasets Molweni and FriendsQA show that our approach yields significant improvements, compared to BERT and ELECTRA baselines. It achieves 73.6% and 77.2% F1-scores on Molweni and FriendsQA, respectively, outperforming state-of-the-art methods that employ different pretrained language models as backbones.

## introduction mdrc is a special machine reading comprehension (mrc) task. it involves answering questions conditioned on the utterances of multiple interlocutors @xcite . mdrc presents unique challenges due to two aspects: ## approach the overall architecture of glgr-based mdrc model is shown in figure 2 . first of all, we utilize plm to initialize the representation of the question, interlocutors and utterances. on this basis, the firststage graph reasoning is conducted over qiug, where graph attention network (gat) @xcite is used for encoding. subsequently, we carry out the second-stage graph reasoning over lsrg, where graph transformer layers @xcite are used for encoding. finally, we concate-nate the initial hidden states and their updated versions obtained by glgr, and feed them into the pointer network for answer prediction. in the following subsections, we detail computational details after presenting the task definition. ## conclusion we propose a global-to-local graph reasoning approach towards mdrc. it explores attentive clues for reasoning in both coarse-grained graph qiug and fine-grained graph lsrg. experimental results show that the proposed approach outperforms the plms baselines and state-of-the-art models. the codes are available at https://github.com/ yanlingli-ai/glgr . the main contribution of this study is to jointly use global conversational structures and local semantic structures during encoding. however, it can be only implemented by two-stage reasoning due to the bottleneck of in-coordinate interaction between heterogeneous graphs. to overcome the issue, we will use the pretrained heterogeneous graph transformers (hgt) for encoding in the future. besides, the graph-structure based pretraining tasks will be designed for task-specific transfer learning.