Learning joint representation for community question answering with tri-modal DBM

Baolin Peng, Wenge Rong, Yuanxin Ouyang, Chao Li, Zhang Xiong

Abstract: One of the main research tasks in Community question answering (CQA) is to find most relevant questions for a given new query, thereby providing useful knowledge for the users. Traditionally used methods such as bag-of-words or latent semantic models consider queries, questions and answers in a same feature space. However, the correlations among queries, questions and answers imply that they lie in different feature spaces. In light of these issues, we proposed a tri-modal deep boltzmann machine (tri-DBM) to extract unified representation for query, question and answer. Experiments on Yahoo! Answers dataset reveal using these unified representation to train a classifier judging semantic matching level between query and question outperforms models using bag-of-words or LSA representation significantly.