Convolutional Neural Tensor Network Architecture for Community-Based Question Answering
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1305-1310.
Abstract
Retrieving similar questions is very important in community-based question answering. A major challenge is the lexical gap in sentence matching. In this paper, we propose a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer. Our model integrates sentence modeling and semantic matching into a single model, which can not only capture the useful information with convolutional and pooling layers, but also learn the matching metrics between the question and its answer. Besides, our model is a general architecture, with no need for the other knowledge such as lexical or syntactic analysis. The experimental results shows that our method outperforms the other methods on two matching tasks.
@inproceedings{ijcai2015p188,
title = {Convolutional Neural Tensor Network Architecture for Community-Based Question Answering},
author = {Qiu, Xipeng and Huang, Xuanjing},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {1305--1310},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/188},
}
TY - CPAPER TI - Convolutional Neural Tensor Network Architecture for Community-Based Question Answering AU - Qiu, Xipeng AU - Huang, Xuanjing T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 1305 EP - 1310 UR - https://ijcai.org/proceedings/2015/188 ER -Copied
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