Jiajun Cheng


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FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering
Sheng Zhang | Jiajun Cheng | Hui Wang | Xin Zhang | Pei Li | Zhaoyun Ding
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3). Convolutional neural networks and bi-directional long-short term memory networks are applied in our methods to extract semantic information from questions and answers (comments). In addition, in order to take the full advantage of question-comment semantic relevance, we deploy interaction layer and augmented features before calculating the similarity. The results show that our methods have the great effectiveness for both subtask A and subtask C.