Consensus Attention-based Neural Networks for Chinese Reading Comprehension

TL;DR

This paper introduces new Chinese reading comprehension datasets and proposes a consensus attention-based neural network that outperforms existing models, establishing a baseline for future research in Chinese NLP reading comprehension tasks.

Contribution

The paper presents the first Chinese reading comprehension datasets and a novel consensus attention neural network architecture for Cloze-style tasks.

Findings

The proposed model significantly outperforms state-of-the-art baselines.

New Chinese reading comprehension datasets are introduced.

A baseline for Chinese reading comprehension is established.

Abstract

Reading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children's Fairy Tale (CFT) dataset. Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension problem, which aims to induce a consensus attention over every words in the query. Experimental results show that the proposed neural network significantly outperforms the state-of-the-art baselines in several public datasets. Furthermore, we setup a baseline for Chinese reading comprehension task, and hopefully this would speed up the process for future research.