Efficacy of Graph-based Text Representations in Machine Reading Comprehension

Abstract

The graph-based representation of a text that properly captures its linguistic structures has been the main concern in natural language processing. It attracts more researchers recently, as a graph is an explicit symbolic representation that can be nicely combined with external knowledge resources. We explore the efficacy of graph-based text representations by devising and comparing reading comprehension models. Specifically, we construct the graph-based representation of an input text by basing the dependency structures of sentences and enhancing them with several methods that add inter-sentence edges. The resulting edge-rich graph is then fed into a graph convolution network to acquire a vector representation that is essential in solving the target multi-choice reading comprehension task. The experimental results suggest that the proposed graph-based model is promising and may contribute to further improve the performance by being coupled with the model relying on a large-scaled pre-trained language model.