Paper Info
Title
Lattice CNNs for Matching Based Chinese Question Answering.
Abstract
Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly. In this paper, we propose a novel lattice based CNN model (LCNs) to utilize multi-granularity information inherent in the word lattice while maintaining strong ability to deal with the introduced noisy information for matching based question answering in Chinese. We conduct extensive experiments on both document based question answering and knowledge based question answering tasks, and experimental results show that the LCNs models can significantly outperform the state-of-the-art matching models and strong baselines by taking advantages of better ability to distill rich but discriminative information from the word lattice input.
Year
Venue
Field
2019
THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
Question answering,Lattice (order),Computer science,Artificial intelligence,Document-based question,Discriminative model,Machine learning
DocType
Volume
Citations 
Journal
abs/1902.09087
1
PageRank 
References 
Authors
0.35
11
6
Name
Order
Citations
PageRank
Yuxuan Lai122.39
Yansong Feng273564.17
Xiaohan Yu327.79
Zheng Wang47910.37
kun xu520320.29
Dongyan Zhao699896.35