Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
Computation and Language
2015-06-23 v1 Information Retrieval
Machine Learning
Abstract
In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of question-answer pair firstly, and then uses the joint representation as input of the long short-term memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.
Cite
@article{arxiv.1506.06490,
title = {Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering},
author = {Xiaoqiang Zhou and Baotian Hu and Qingcai Chen and Buzhou Tang and Xiaolong Wang},
journal= {arXiv preprint arXiv:1506.06490},
year = {2015}
}
Comments
6 pages