Affiliation:
1. National University of Defense Technology, Changsha, Hunan, China
Abstract
Community Q&A forum is a special type of social media that provides a platform to raise questions and to answer them (both by forum participants), to facilitate online information sharing. Currently, community Q&A forums in professional domains have attracted a large number of users by offering professional knowledge. To support information access and save users’ efforts of raising new questions, they usually come with a
question retrieval
function, which retrieves similar existing questions (and their answers) to a user’s query. However, it can be difficult for community Q&A forums to cover all domains, especially those emerging lately with little labeled data but great discrepancy from existing domains. We refer to this scenario as cross-domain question retrieval. To handle the unique challenges of cross-domain question retrieval, we design a model based on adversarial training, namely,
X-QR
, which consists of two modules—a domain discriminator and a sentence matcher. The domain discriminator aims at aligning the source and target data distributions and unifying the feature space by domain-adversarial training. With the assistance of the domain discriminator, the sentence matcher is able to learn domain-consistent knowledge for the final matching prediction. To the best of our knowledge, this work is among the first to investigate the domain adaption problem of sentence matching for community Q&A forums question retrieval. The experiment results suggest that the proposed
X-QR
model offers better performance than conventional sentence matching methods in accomplishing cross-domain community Q&A tasks.
Funder
National Key Research and Development Program of China
NSFC
NSF of Hunan Province
Publisher
Association for Computing Machinery (ACM)
Reference54 articles.
1. SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK
2. Li Cai, Guangyou Zhou, Kang Liu, and Jun Zhao. 2011. Learning the latent topics for question retrieval in community QA. In Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP’11). ACL, 273–281.
3. A Semantic Graph based Topic Model for Question Retrieval in Community Question Answering
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