GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension

Yu Chen, Lingfei Wu, Mohammed J. Zaki

Keywords: deep learning, graph neural networks, natural language processing, reading comprehension

Abstract: Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However, most existing approaches do not effectively capture conversation history and thus have trouble handling questions involving coreference or ellipsis. We propose a novel graph neural network (GNN) based model, namely GraphFlow, which captures conversational flow in the dialog. Specifically, we first propose a new approach to dynamically construct a question-aware context graph from passage text at each turn. We then present a novel flow mechanism to model the temporal dependencies in the sequence of context graphs. The proposed GraphFlow model shows superior performance compared to existing state-of-the-art methods. For instance, GraphFlow outperforms two recently proposed models on the CoQA benchmark dataset: FlowQA by 2.3% and SDNet by 0.7% on F1 score, respectively. In addition, visualization experiments show that our proposed model can better mimic the human reasoning process for conversational MC compared to existing models.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1908.00059/code)