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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- Adversarial Examples for Evaluating Reading Comprehension Systems
- Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
- Phase Conductor on Multi-layered Attentions for Machine Comprehension
- 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