CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Nan Duan; Ming Zhou; Duyu Tang; Ming Gong; Daxin Jiang; Bing Qin; Zhangyin Feng; Xiaocheng Feng; Daya Guo; Linjun Shou; Ting Liu

doi:10.18653/v1/2020.findings-emnlp.139

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

CodeBERT: предобученная модель для языков программирования и естественных языков

Nan Duan, Ming Zhou, Duyu Tang, Ming Gong, Daxin Jiang, Bing Qin, Zhangyin Feng, Xiaocheng Feng, Daya Guo, Linjun Shou, Ting Liu

2020-01-01

SCID: 54.1/7kpxzjbd Discuss with AI

CodeBERTnatural languagespre-trained modelprogramming languages

Abstract (AI)

Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou. Findings of the Association for Computational Linguistics: EMNLP 2020. 2020.

Key Findings

The provided text contains bibliographic information but no scientific abstract or results to support extracting substantive findings.

The title indicates CodeBERT concerns a pre-trained model for programming and natural languages, but provides no methodological or empirical details.

Research Object

CodeBERT pre-trained model for programming and natural languages

Research Subject

Cross-modal understanding and generation between programming languages and natural languages

Publication Details

DOI

10.18653/v1/2020.findings-emnlp.139

Publication Date

2020-01-01

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Publisher

ISSN

Cited by

2667

Open access PDF

https://www.aclweb.org/anthology/2020.findings-…

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Author Information

Authors

Nan Duan

Ming Zhou

Duyu Tang

Ming Gong

Daxin Jiang

Bing Qin

Zhangyin Feng

Xiaocheng Feng

Daya Guo

Linjun Shou

Ting Liu

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References available in scid.ai7

Exploiting Generative AI to Scale up Intelligent Tutoring Systems2023 AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale2018 Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation2014 Convolutional Neural Networks for Sentence Classification2014 Sequence to Sequence Learning with Neural Networks2014 Exploring the Limits of Transfer Learning with a Unified Text-to-Text\n Transformer2019 Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation2016

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