Cited by 674Software Engineering ResearchTopic ModelingTeaching and Learning Programming

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UniXcoder: Unified Cross-Modal Pre-training for Code Representation

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Guo, Daya, Lu, Shuai, Duan, Nan, Wang, Yanlin, Zhou, Ming, Yin, Jian·2022·Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

DOI 10.18653/v1/2022.acl-long.499

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Abstract

Pre-trained models for programming languages have recently demonstrated great success on code intelligence.To support both code-related understanding and generation tasks, recent works attempt to pre-train unified encoder-decoder models.However, such encoder-decoder framework is sub-optimal for auto-regressive tasks, especially code completion that requires a decoder-only manner for efficient inference.In this paper, we present UniXcoder, a unified cross-modal pre-trained model for programming language.The model utilizes mask attention matrices with prefix adapters to control the behavior of the model and leverages cross-modal contents like AST and code comment to enhance code representation.To encode AST that is represented as a tree in parallel, we propose a one-to-one mapping method to transform AST in a sequence structure that retains all structural information from the tree.Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task.We evaluate UniXcoder on five code-related tasks over nine datasets.To further evaluate the performance of code fragment representation, we also construct a dataset for a new task, called zero-shot code-to-code search.Results show that our model achieves state-of-the-art performance on most tasks and analysis reveals that comment and AST can both enhance UniXcoder.

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Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
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Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
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Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
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Show all 28 references

Research works printed but not linked: 2

Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, LIU Shujie, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al. 2020. Graphcode- bert: Pre-training code representations with data flow. In International Conference on Learning Rep- resentations.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Lan- guage models are unsupervised multitask learners. OpenAI blog, 1(8):9.

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Authors: Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., & Yin, J.
Affiliations: Sun Yat-sen University (CN)
Microsoft Research Asia (China) (CN)
ORCID: Guo, Daya
Lu, Shuai
Duan, Nan
Wang, Yanlin
Zhou, Ming
Yin, Jian
Venue: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher: Association for Computational Linguistics
Published: 2022
Pages: 7212-7225
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/2022.acl-long.499
OpenAlex: W4221166942
Cited by: 673
References: 28
Citations by year: 2026: 153 · 2025: 239 · 2024: 148 · 2023: 122 · 2022: 11
FWCI: 116.24
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
Funders: National Natural Science Foundation of China (U1811262, U1811261, U2001211, U1811264, U1911203)
Basic and Applied Basic Research Foundation of Guangdong Province (2019B15 15130001)
Topics: Physical Sciences › Computer Science › Information Systems › Software Engineering Research
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
Keywords: UniXcoder, code representation, programming language models, code comments, contrastive learning, cross-modal generation, code completion, code intelligence

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UniXcoder: Unified Cross-Modal Pre-training for Code Representation

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