|Title||Question Condensing Networks for Answer Selection in Community Question Answering|
|Affiliation||Peking Univ, Key Lab Computat Linguist, MOE, Beijing 100871, Peoples R China.|
Collaborat Innovat Ctr Language Abil, Xuzhou 221009, Jiangsu, Peoples R China.
|Publisher||PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1|
|Citation||PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1. 2018, 1746-1755.|
|Abstract||Answer selection is an important subtask of community question answering (CQA). In a real-world CQA forum, a question is often represented as two parts: a subject that summarizes the main points of the question, and a body that elaborates on the subject in detail. Previous researches on answer selection usually ignored the difference between these two parts and concatenated them as the question representation. In this paper, we propose the Question Condensing Networks (QCN) to make use of the subject-body relationship of community questions. In this model, the question subject is the primary part of the question representation, and the question body information is aggregated based on similarity and disparity with the question subject. Experimental results show that QCN outperforms all existing models on two CQA datasets.|
|Appears in Collections:||计算语言学教育部重点实验室|