Duplicate Question Detection With Deep Learning in Stack Overflow

作     者：Wang, Liting Zhang, Li Jiang, Jing 

作者机构：Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2020年第8卷

页      面：25964-25975页

核心收录：

基　　金：National Key Research and Development Program of China [2018AAA0102304] National Natural Science Foundation of China State Key Laboratory of Software Development Environment [SKLSDE-2019ZX-05] 

主　　题：Stack overflow duplicate question detection deep learning 

摘      要：Stack Overflow is a popular Community-based Question Answer (CQA) website focused on software programming and has attracted more and more users in recent years. However, duplicate questions frequently appear in Stack Overflow and they are manually marked by the users with high reputation. Automatic duplicate question detection alleviates labor and effort for users with high reputation. Although existing approaches extract textual features to automatically detect duplicate questions, these approaches are limited since semantic information could be lost. To tackle this problem, we explore the use of powerful deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), to detect duplicate questions in Stack Overflow. In addition, we use Word2Vec to obtain the vector representations of words. They can fully capture semantic information at document-level and word-level respectively. Therefore, we construct three deep learning approaches WV-CNN, WV-RNN and WV-LSTM, which are based on Word2Vec, CNN, RNN and LSTM, to detect duplicate questions in Stack Overflow. Evaluation results show that WV-CNN and WV-LSTM have made significant improvements over four baseline approaches (i.e., DupPredictor, Dupe, DupPredictorRep-T, and DupeRep) and three deep learning approaches (i.e., DQ-CNN, DQ-RNN, and DQ-LSTM) in terms of recall-rate@5, recall-rate@10 and recall-rate@20. Furthermore, the experimental results indicate that our approaches WV-CNN, WV-RNN, and WV-LSTM outperform four machine learning approaches based on Support Vector Machine, Logic Regression, Random Forest and eXtreme Gradient Boosting in terms of recall-rate@5, recall-rate@10 and recall-rate@20.