Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
arXiv:1703.04617
Abstract
The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions. We then view and model different types of questions and the information shared among them as an adaptation task and proposed adaptation models for them. On the Stanford Question Answering Dataset (SQuAD), we show that these approaches can help attain better results over a competitive baseline.
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- Reinforced Mnemonic Reader for Machine Reading Comprehension
- Stochastic Answer Networks for Machine Reading Comprehension
- Neural Machine Reading Comprehension: Methods and Trends
- The Effect of Natural Distribution Shift on Question Answering Models
- Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
- Phase Conductor on Multi-layered Attentions for Machine Comprehension
- What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text
- Ruminating Reader: Reasoning with Gated Multi-Hop Attention
- Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks
- Several Experiments on Investigating Pretraining and Knowledge-Enhanced Models for Natural Language Inference
- An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks