  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.
