A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension

Abstract

In this paper, we introduce Adversarial-and-attention Network (A3Net) for Machine Reading Comprehension. This model extends existing approaches from two perspectives. First, adversarial training is applied to several target variables within the model, rather than only to the inputs or embeddings. We control the norm of adversarial perturbations according to the norm of original target variables, so that we can jointly add perturbations to several target variables during training. As an effective regularization method, adversarial training improves robustness and generalization of our model. Second, we propose a multi-layer attention network utilizing three kinds of high-efficiency attention mechanisms. Multi-layer attention conducts interaction between question and passage within each layer, which contributes to reasonable representation and understanding of the model. Combining these two contributions, we enhance the diversity of dataset and the information extracting ability of the model at the same time. Meanwhile, we construct A3Net for the WebQA dataset. Results show that our model outperforms the state-of-the-art models (improving Fuzzy Score from 73.50% to 77.0%).

arXiv commentNLPCC2018, 12 pages

References in corpus (9)

• Teaching Machines to Read and Comprehend
• SQuAD: 100,000+ Questions for Machine Comprehension of Text
• Machine Comprehension Using Match-LSTM and Answer Pointer
• ReasoNet: Learning to Stop Reading in Machine Comprehension
• Adversarial Examples for Evaluating Reading Comprehension Systems
• FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
• Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
• S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
• Making Neural QA as Simple as Possible but not Simpler