Question Condensing Networks for Answer Selection in Community Question Answering

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 our 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.

[1]  Siu Cheung Hui,et al.  Cross Temporal Recurrent Networks for Ranking Question Answer Pairs , 2017, AAAI.

[2]  Yang Xiang,et al.  Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF , 2016, COLING.

[3]  Frank Keller,et al.  Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL , 2014, Conference on Empirical Methods in Natural Language Processing.

[4]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[5]  Preslav Nakov,et al.  Global Thread-level Inference for Comment Classification in Community Question Answering , 2015, EMNLP.

[6]  Preslav Nakov,et al.  Thread-Level Information for Comment Classification in Community Question Answering , 2015, ACL.

[7]  Zhiguo Wang,et al.  Sentence Similarity Learning by Lexical Decomposition and Composition , 2016, COLING.

[8]  Alexander M. Rush,et al.  Character-Aware Neural Language Models , 2015, AAAI.

[9]  Preslav Nakov,et al.  SemEval-2017 Task 3: Community Question Answering , 2017, *SEMEVAL.

[10]  Rob Fergus,et al.  Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[11]  Quan Hung Tran,et al.  JAIST: Combining multiple features for Answer Selection in Community Question Answering , 2015, *SEMEVAL.

[12]  Preslav Nakov,et al.  SemEval-2016 Task 3: Community Question Answering , 2019, *SEMEVAL.

[13]  Preslav Nakov,et al.  Joint Learning with Global Inference for Comment Classification in Community Question Answering , 2016, NAACL.

[14]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[15]  Houfeng Wang,et al.  Attentive Interactive Neural Networks for Answer Selection in Community Question Answering , 2017, AAAI.

[16]  Wei Wu,et al.  Bi-directional Gated Memory Networks for Answer Selection , 2017, CCL.

[17]  Alessandro Moschitti,et al.  KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering , 2017, *SEMEVAL.

[18]  Man Lan,et al.  ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task , 2017, SemEval@ACL.

[19]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[20]  Xiaolong Wang,et al.  HITSZ-ICRC: Exploiting Classification Approach for Answer Selection in Community Question Answering , 2015, *SEMEVAL.

[21]  Tao Shen,et al.  DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding , 2017, AAAI.

[22]  Preslav Nakov,et al.  SemEval-2015 Task 3: Answer Selection in Community Question Answering , 2015, *SEMEVAL.

[23]  Roberto Basili,et al.  KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers , 2016, *SEMEVAL.

[24]  Shuohang Wang,et al.  A Compare-Aggregate Model for Matching Text Sequences , 2016, ICLR.

[25]  Zhoujun Li,et al.  Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering , 2017, *SEMEVAL.