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Stack Overflow is often viewed as the most influential Software Question Answer (SQA) website with millions of programming-related questions and answers.
SciBERT: A Pretrained Language Model for Scientific Text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 1903
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ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Kexin Huang, Jaan Altosaar, and Rajesh Ranganath. 2020 · 1904
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How to Fine-Tune BERT for Text Classification?
Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. 2020 · 1905
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Nils Reimers and Iryna Gurevych. 2019 · 1908
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CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2020 · 1909
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 1909
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IntelliCode Compose: Code Generation Using Transformer
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020 · 2005
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Code and Named Entity Recognition in StackOverflow
Jeniya Tabassum, Mounica Maddela, Wei Xu, and Alan Ritter. 2020 · 2005
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Tag recommendation in software information sites. In 2013 10th Working Conference on Mining Software Repositories (MSR) . IEEE, 287–296
Xin Xia, David Lo, Xinyu Wang, and Bo Zhou. 2013b · 2013
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What are developers talking about? an analysis of topics and trends in stack overflow
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EnTagRec: An Enhanced Tag Recommendation System for Software Information Sites. In 2014 IEEE International Conference on Software Maintenance and Evolution . 291–300
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A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Lisbon, Portugal, 632–642
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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The JIRA Repository Dataset: Understanding Social Aspects of Software Development
Marco Ortu, Giuseppe Destefanis, Alessandro Murgia, Roberto Tonelli, Michele Marchesi, and Bram Adams. 2015 · 2015
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Deep learning in neural networks: An overview
Jürgen Schmidhuber. 2015 · 2015
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Tagcombine: Recommending tags to contents in software information sites
Xin-Yu Wang, Xin Xia, and David Lo. 2015a · 2015
Cited alongside, same era.
Tagcombine: Recommending tags to contents in software information sites
Xin-Yu Wang, Xin Xia, and David Lo. 2015b · 2015
Cited alongside, same era.
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Scalable tag recommendation for software information sites. In 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 272–282
CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 . Association for Computational Linguistics, Online, 1536–1547
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020 · 2020
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Is bert really robust? a strong baseline for natural language attack on text classification and entailment. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 8018–8025
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
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Learning and Evaluating Contextual Embedding of Source Code. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, 5110–5121
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TagDC: A tag recommendation method for software information sites with a combination of deep learning and collaborative filtering
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Pingyi Zhou, Jin Liu, Zijiang Yang, and Guangyou Zhou. 2017 · 2017
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
EnTagRec ++: An enhanced tag recommendation system for software information sites
Shaowei Wang, David Lo, Bogdan Vasilescu, and Alexander Serebrenik. 2018a · 2018
Cited alongside, same era.
EnTagRec++: An enhanced tag recommendation system for software information sites
Shaowei Wang, David Lo, Bogdan Vasilescu, and Alexander Serebrenik. 2018b · 2018
Cited alongside, same era.
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . Association for Computational Linguistics, New Orleans, Louisiana, 1112–1122
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Cited alongside, same era.
BERT with history answer embedding for conversational question answering. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval . 1133–1136
Chen Qu, Liu Yang, Minghui Qiu, W Bruce Croft, Yongfeng Zhang, and Mohit Iyyer. 2019 · 2019
Cited alongside, same era.
Can Li, Ling Xu, Meng Yan, and Yan Lei. 2020 · 2020
Later among the works it cites.
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020 · 2020
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Sentiment analysis for software engineering: How far can pre-trained transformer models go?. In 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 70–80
Ting Zhang, Bowen Xu, Ferdian Thung, Stefanus Agus Haryono, David Lo, and Lingxiao Jiang. 2020 · 2020
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CoSQA: 20, 000+ Web Queries for Code Search and Question Answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021 , Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 5690–5700
Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, and Nan Duan. 2021 · 2021
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Traceability transformed: Generating more accurate links with pre-trained BERT models. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 324–335
Jinfeng Lin, Yalin Liu, Qingkai Zeng, Meng Jiang, and Jane Cleland-Huang. 2021 · 2021
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Applying CodeBERT for Automated Program Repair of Java Simple Bugs. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . 505–509
Ehsan Mashhadi and Hadi Hemmati. 2021 · 2021
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Julian von der Mosel, Alexander Trautsch, and Steffen Herbold. 2021 · 2021
Later among the works it cites.
Post2Vec: Learning Distributed Representations of Stack Overflow Posts
Bowen Xu, Thong Hoang, Abhishek Sharma, Chengran Yang, Xin Xia, and David Lo. 2021 · 2021
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Assessing Generalizability of CodeBERT. In 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 425–436
Xin Zhou, DongGyun Han, and David Lo. 2021 · 2021
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Can Identifier Splitting Improve Open-Vocabulary Language Model of Code?. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE
Jieke Shi, Zhou Yang, Junda He, Bowen Xu, and David Lo. 2022 · 2022
Closest in time.
Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?. In 29th IEEE International Conference onSoftware Analysis, Evolution and Reengineering(SANER) . IEEE
Chengran Yang, Bowen Xu, Junaed Khan Younus, Gias Uddin, Donggyun Han, Zhou Yang, and David Lo. 2022 · 2022
Closest in time.