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duplicate question detection in community-based platforms via interaction networks

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第4期83卷 10881-10898页

作者： Gao, Wang Yang, Baoping Xiao, Yue Zeng, Peng Hu, Xi Zhu, Xun Jianghan Univ Sch Artificial Intelligence Wuhan 430056 Peoples R China Jianghan Univ Engn Res Ctr Intelligent Decis & Informat Proc Wuhan 430056 Peoples R China Huanggang Normal Univ Phys & Telecommun Coll Engn Huanggang 438000 Peoples R China

Community-based question and Answering (CQA) platforms have a huge number of users, resulting in numerous duplicate questions with similar intent from different users. Effectively detecting duplicate questions can improve the findability of platforms, and enhance the user experience of viewers and writers. Existing state-of-the-art methods focus on designing the structure of multi-layer interaction networks, ignoring the problems of error propagation and loss of low-level semantics. In this paper, we propose a novel Interaction-based Siamese Network (ISN) to address these issues, which utilizes a siamese structure to learn the original semantics of questions and captures interaction information with question interactive units. During the interaction, each interactive unit takes the original semantic representation of another question as an input, thus effectively mitigating the effect of error propagation. Furthermore, we propose an aggregation strategy to propagate low-level interaction features to high-level to preserve low-level semantic information, and introduce self-attention to enhance the model's global interaction information learning ability. Experimental results on a real-world CQA dataset show that ISN outperforms state-of-the-art models for duplicate question detection.

关键词： duplicate question detection Siamese networks Text matching Interaction network Attention mechanism

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Bert-QAnet: BERT-encoded hierarchical question-answer cross-attention network for duplicate question detection

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NEUROCOMPUTING 2022年 509卷 68-74页

作者： Zhao, Xuan Huang, Jimmy Xiangji York Univ Informat Retrieval & Knowledge Management Res Lab Toronto ON Canada

Community question Answering (CQA) provides platforms for users with different backgrounds to share information and knowledge. With the increasing popularity of CQA, more and more question-answer (Q-A) pairs, with numerous duplicates, have accumulated. Therefore, many researchers focus on detecting duplicate questions in CQA. However, most existing techniques utilize only questions to solve the dupli-cate question detection task, while paired answers which may also contain necessary information are not considered. In this paper, we propose a BERT-encoded Hierarchical question-Answer Cross-Attention Network for duplicate question detection (Bert-QAnet) for detecting duplicate questions. Our model applies BERT to encode text and extract text features. Further, we use cross-attention to integrate word-level features both in question and answer. Also, inner attention is used to capture the interaction between question and answer. Hence, our model Bert-QAnet makes full use of semantic information in paired answers at both word-level and sentence-level. We evaluate our model on two datasets: the Yahoo! Answers dataset and the Stack Overflow dataset. To meet the special requirements of this study, both datasets are extended by paired answers. Experimental results demonstrate that our proposed model achieves state-of-the-art performance.(c) 2022 Elsevier B.V. All rights reserved.

关键词： duplicate question detection Community question answering Cross -attention network Bert encoding

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duplicate question detection With Deep Learning in Stack Overflow

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IEEE ACCESS 2020年 8卷 25964-25975页

作者： Wang, Liting Zhang, Li Jiang, Jing Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China

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.

关键词： Stack overflow duplicate question detection deep learning

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Forum duplicate question detection by Domain Adaptive Semantic Matching

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IEEE ACCESS 2020年 8卷 56029-56038页

作者： Xu, Zhuojia Yuan, Hua South China Univ Technol Sch Comp Sci & Engn Commun & Comp Network Lab Guangdong Guangzhou 510641 Guangdong Peoples R China

Community question Answering (CQA) forums, such as Stack Overflow, Stack Exchange and Massive Open Online Course (MOOC) forums, spend a lot of manpower and time to manage duplicate questions on the forum. Mismatch of duplicate questions makes users keep asking & x201C;new & x201D;questions, and the continuous accumulation of duplicate questions may interfere with their information searching again, affecting user satisfaction. Neural Networks (NN) models for parsing semantics provide the possibility of end-to-end duplicate question detection. Whereas, due to lack of domain data and expertise, NN models for semantic parsing are rarely directly applied to CQA duplicate question detection. This paper proposes a Semantic Matching Model (SMM) integrated with the multi-task transfer learning framework for multi-domain forum duplicate question detection. By designing the word-to-sentence interaction mechanism based on the word-to-word interaction, SMM can automatically choose to ignore or pay attention to potential similar words according to the semantics at the sentence level. The experiments on the benchmark data set and MOOC forum data set state that SMM outperforms baselines, its interaction mechanism is effective and it has an advantage in cross-domain duplicate question detection.

关键词： Community question answering duplicate question detection semantic matching transfer learning

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Adaptive Multi-Attention Network Incorporating Answer Information for duplicate question detection 19

Adaptive Multi-Attention Network Incorporating Answer Inform...

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42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Liang, Di Zhang, Fubao Zhang, Weidong Zhang, Qi Fu, Jinlan Peng, Minlong Gui, Tao Huang, Xuanjing Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 201203 Peoples R China

ISBN: (纸本)9781450361729

Community-based question answering (CQA), which provides a platform for people with diverse backgrounds to share information and knowledge, has become increasingly popular. With the accumulation of site data, methods to detect duplicate questions in CQA sites have attracted considerable attention. Existing methods typically use only questions to complete the task. However, the paired answers may also provide valuable information. In this paper, we propose an answer information- enhanced adaptive multi-attention network (AMAN) to perform this task. AMAN takes full advantage of the semantic information in the paired answers while alleviating the noise problem caused by adding the answers. To evaluate the proposed method, we use a CQADupStack set and the Quora question-pair dataset expanded with paired answers. Experimental results demonstrate that the proposed model can achieve state-of-the-art performance on the above two data sets.

关键词： duplicate question detection adaptive multi-attention community-based question answering

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Learning Profiles in duplicate question detection 18

Learning Profiles in Duplicate Question Detection

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IEEE 18th International Conference on Information Reuse and Integration (IRI)

作者： Saedi, Chakaveh Rodrigues, Joao Silva, Joao Branco, Antonio Maraev, Vladislav Univ Lisbon Dept Informat P-1749016 Lisbon Portugal

ISBN: (纸本)9781538615621

This paper presents the results of systematic and comparative experimentation with major types of methodologies for automatic duplicate question detection when these are applied to datasets of progressively larger sizes, thus allowing to study the learning profiles of this task under these different approaches and evaluate their merits. This study was made possible by resorting to the recent release for research purposes, by the Quora online question answering engine, of a new dataset with over 400,000 pairs labeled with respect to their elements being duplicate interrogative segments.

关键词： Natural language processing semantic similarity duplicate question detection machine learning neural networks deep learning

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Detecting duplicate questions in Stack Overflow via Source Code Modeling

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2022年第2期32卷 227-255页

作者： Gao, Wei Wu, Jian Xu, Guandong Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Univ Technol Sydney Adv Analyt Inst Sydney NSW Australia

Stack Overflow is one of the most popular question-Answering sites for programmers. However, it faces the problem of question duplication, where newly created questions are identical to previous questions. Existing works on duplicate question detection in Stack Overflow extract a set of textual features on the question pairs and use supervised learning approaches to classify duplicate question pairs. However, they do not consider the source code information in the questions. While in some cases, the intention of a question is mainly represented by the source code. In this paper, we aim to learn the semantics of a question by combining both text features and source code features. We use word embedding and convolutional neural networks to extract textual features from questions to overcome the lexical gap issue. We use tree-based convolutional neural networks to extract structural and semantic features from source code. In addition, we perform multi-task learning by combining the duplication question detection task with a question tag prediction side task. We conduct extensive experiments on the Stack Overflow dataset and show that our approach can detect duplicate questions with higher recall and MRR compared with baseline approaches on Python and Java programming languages.

关键词： Community question answering duplicate question detection stack overflow

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Detecting duplicate Multiple Choice questions in the Large question Bank 15th

Detecting Duplicate Multiple Choice Questions in the Large Q...

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15th International Conference on Computational Collective Intelligence (ICCCI)

作者： Phan Duc Manh Nguyen Tuan Minh Phan Duy Hung FPT Univ Hanoi Vietnam

ISBN: (纸本)9783031414558;9783031414565

A question bank is a database of questions in a variety of formats used as a central repository for building tests. question banks are the core tool for innovative testing and assessment of learners' learning outcomes. It can be built and added over time from many sources and by many people. So creating new questions may result in duplicate questions, and deciding whether to include that question in the real database will take time to manually search across data files. This study proposes an end-to-end machine learning architecture to combine the information from the text data and the data from the image in question through optical character recognition into an encoded vector. From there, the system can query to find similar questions based on similarity ranking. The machine learning model was evaluated on a part of the question bank at FPT University, a university of information technology in Vietnam. The obtained F1 score of 0.95 proves that the model can be applied to intelligently manage the question bank of the FPT education system as well as that of other educational institutions.

关键词： question Bank duplicate question detection Similarity Score

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Improving Student Forum Responsiveness: Detecting duplicate questions in Educational Forums 26th

Improving Student Forum Responsiveness: Detecting Duplicate ...

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Mohania, Manal Zhou, Liyuan Gedeon, Tom Australian Natl Univ Coll Engn & Comp Sci Canberra ACT Australia

ISBN: (纸本)9783030367183;9783030367176

Student forums are important for student engagement and learning in university courses but require high staff resources to moderate and answer questions. In introductory courses, the content can remain almost unchanged each year, so the questions asked in the course forums do not see a lot of variety over different iterations, which provides an opportunity for automation. This paper compiles a dataset of forum threads and meta-information of the participants from the Web Design and Development course at the Australian National University for the purposes of duplicate question detection in educational forums. A state of the art neural network model is trained on the dataset to measure its usefulness. An accuracy of 91.8% is achieved, which is on par with what is achieved on other datasets with similar features. A high performing neural network for this dataset could potentially be used to create a live system that detects and reuses answers for duplicate questions on course forums.

关键词： duplicate question detection Neural networks duplicate question pair dataset

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Semantic Equivalence detection: Are Interrogatives Harder than Declaratives? 11

Semantic Equivalence Detection: Are Interrogatives Harder th...

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11th International Conference on Language Resources and Evaluation (LREC)

作者： Rodrigues, Joao Saedi, Chakaveh Branco, Antonio Silva, Joao Univ Lisbon NLX Nat Language & Speech Grp Dept Informat Fac Ciencias P-1749016 Lisbon Portugal

ISBN: (纸本)9791095546009

duplicate question detection (DQD) is a Natural Language Processing task under active research, with applications to fields like Community question Answering and Information Retrieval. While DQD falls under the umbrella of Semantic Text Similarity (STS), these are often not seen as similar tasks of semantic equivalence detection, with STS being implicitly understood as concerning only declarative sentences. Nevertheless, approaches to STS have been applied to DQD and paraphrase detection, that is to interrogatives and declaratives, alike. We present a study that seeks to assess, under conditions of comparability, the possible different performance of state-of-the-art approaches to STS over different types of textual segments, including most notably declaratives and interrogatives. This paper contributes to a better understanding of current mainstream methods for semantic equivalence detection, and to a better appreciation of the different results reported in the literature when these are obtained from different data sets with different types of textual segments. Importantly, it contributes also with results concerning how data sets containing textual segments of a certain type can be used to leverage the performance of resolvers for segments of other types.

关键词： semantic text similarity paraphrase detection duplicate question detection

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