Joint modeling of users, questions and answers for answer selection in CQA

Abstract In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA). Automatically selecting correct answers can significantly improve intelligence for CQA, as users are not required to browse the large quantity of texts and select the right answers manually. Also, automatic answers selection can minimize the time for satisfying users seeking the correct answers and maximize user engagement with the site. Unlike previous works, we propose a hybrid attention mechanism to model question-answer pairs. Specifically, for each word, we calculate the intra-sentence attention indicating its local importance and the inter-sentence attention implying its importance to the counterpart sentence. The inter-sentence attention is based on the interactions between question-answer pairs, and the combination of these two attention mechanisms enables us to align the most informative parts in question-answer pairs for sentence matching. Additionally, we exploit user information for answer selection due to the fact that users are more likely to provide correct answers in their areas of expertise. We model users from their written answers to alleviate data sparsity problem, and then learn user representations according to the informative parts in sentences that are useful for question-answer matching task. This mean of modelling users can bridge the semantic gap between different users, as similar users may have the same way of wording their answers. The representations of users, questions and answers are learnt in an end-to-end neural network in a mean that best explains the interrelation between question-answer pairs. We validate the proposed model on a public dataset, and demonstrate its advantages over the baselines with thorough experiments.

[1]  Alessandro Moschitti,et al.  Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks , 2015, SIGIR.

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

[3]  Xuanjing Huang,et al.  Convolutional Neural Tensor Network Architecture for Community-Based Question Answering , 2015, IJCAI.

[4]  Yong Zhang,et al.  Multiview Convolutional Neural Networks for Multidocument Extractive Summarization , 2017, IEEE Transactions on Cybernetics.

[5]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[6]  David van Dijk,et al.  Early Detection of Topical Expertise in Community Question Answering , 2015, SIGIR.

[7]  Lei Zheng,et al.  Joint Deep Modeling of Users and Items Using Reviews for Recommendation , 2017, WSDM.

[8]  Hang Li,et al.  Convolutional Neural Network Architectures for Matching Natural Language Sentences , 2014, NIPS.

[9]  Yong Zhang,et al.  Attention pooling-based convolutional neural network for sentence modelling , 2016, Inf. Sci..

[10]  Di Wang,et al.  A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering , 2015, ACL.

[11]  Xuanjing Huang,et al.  Retweet Prediction with Attention-based Deep Neural Network , 2016, CIKM.

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

[13]  Xueqi Cheng,et al.  A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations , 2015, AAAI.

[14]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[15]  Roberto Basili,et al.  Exploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification , 2007, ACL.

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

[17]  Alessandro Moschitti,et al.  Automatic Feature Engineering for Answer Selection and Extraction , 2013, EMNLP.

[18]  Yueting Zhuang,et al.  Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning , 2017, AAAI.

[19]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

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

[21]  W. Bruce Croft,et al.  aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model , 2016, CIKM.

[22]  Chris Callison-Burch,et al.  Answer Extraction as Sequence Tagging with Tree Edit Distance , 2013, NAACL.

[23]  Nan Jiang,et al.  Word Embedding Based Correlation Model for Question/Answer Matching , 2015, AAAI.

[24]  Zhongfei Zhang,et al.  DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks , 2016, KDD.

[25]  Xiaodong He,et al.  A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems , 2015, WWW.

[26]  Alessandro Moschitti,et al.  Assessing the Impact of Syntactic and Semantic Structures for Answer Passages Reranking , 2015, CIKM.

[27]  Siu Cheung Hui,et al.  Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture , 2017, SIGIR.