Explicit Utilization of General Knowledge in Machine Reading Comprehension
arXiv:1809.03449
Abstract
To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. Based on the data enrichment method, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. When only a subset (20%-80%) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise.
arXiv commentACL 2019
References in corpus (18)
- Adam: A Method for Stochastic Optimization
- NLTK: The Natural Language Toolkit
- Bidirectional Attention Flow for Machine Comprehension
- Convolutional Neural Networks for Sentence Classification
- ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
- SQuAD: 100,000+ Questions for Machine Comprehension of Text
- Dynamic Coattention Networks For Question Answering
- QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
- ReasoNet: Learning to Stop Reading in Machine Comprehension
- Adversarial Examples for Evaluating Reading Comprehension Systems
- Reading Wikipedia to Answer Open-Domain Questions
- MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
- Dynamic Integration of Background Knowledge in Neural NLU Systems
- Densely Connected Attention Propagation for Reading Comprehension
- Reinforced Mnemonic Reader for Machine Reading Comprehension
- Stochastic Answer Networks for Machine Reading Comprehension
- Semi-Supervised QA with Generative Domain-Adaptive Nets
- Structural Embedding of Syntactic Trees for Machine Comprehension