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

TL;DR

GraphFlow introduces a dynamic graph neural network approach that models conversational flow and context in machine comprehension, improving understanding of dialogue history and semantic relationships for better performance.

Contribution

It presents a novel Recurrent Graph Neural Network with a flow mechanism to capture conversational flow and context-aware graphs, advancing conversational machine comprehension.

Findings

Achieves competitive results on CoQA, QuAC, and DoQA benchmarks.

Provides interpretability through visualization of reasoning process.

Effectively models conversation history and semantic relationships.

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. Moreover, when reasoning over passage text, most of them simply treat it as a word sequence without exploring rich semantic relationships among words. In this paper, we first propose a simple yet effective graph structure learning technique to dynamically construct a question and conversation history aware context graph at each conversation turn. Then we propose a novel Recurrent Graph Neural Network, and based on that, we introduce a flow mechanism to model the temporal dependencies in a sequence of context graphs. The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks. In addition, visualization experiments show that our proposed model can offer good interpretability for the reasoning process.