Applying Deep Learning to Answer Selection: A Study and An Open Task
Computation and Language
2015-10-05 v2 Machine Learning
Abstract
We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3% on a test set, which indicates a great potential for practical use.
Cite
@article{arxiv.1508.01585,
title = {Applying Deep Learning to Answer Selection: A Study and An Open Task},
author = {Minwei Feng and Bing Xiang and Michael R. Glass and Lidan Wang and Bowen Zhou},
journal= {arXiv preprint arXiv:1508.01585},
year = {2015}
}
Comments
To appear in the proceedings of ASRU 2015