Unified Generative Adversarial Networks for Multiple-Choice Oriented Machine Comprehension

Abstract

In this article, we address the multiple-choice machine comprehension (MC) problem in natural language processing. Existing approaches for MC are usually designed for general cases; however, we specially develop a novel method for solving the multiple-choice MC problem. We take the inspiration generative adversarial networks (GANs) and first propose an adversarial framework for multiple-choice oriented MC, named McGAN. Specifically, our approach is designed as a GAN-based method that unifies both generative and discriminative MC models. Working together, the generative model focuses on predicting relevant answer given a passage (text) and a question; the discriminative model focuses on predicting their relevancy given an answer-passage-question set. Based on the competition via adversarial training in a minimize-maximize game, the proposed method takes advantages from both models. To evaluate the performance, we test our McGAN model on three well-known datasets for multiple-choice MC. Our results show that McGAN can achieve a significant increase in accuracy compared to existing models based on all three datasets, and it consistently outperforms all tested baselines, including state-of-the-art techniques.