LenANet: A Length-Controllable Attention Network for Source Code Summarization

Author:

Chen Peng,Wu Shaojuan,Chen Ziqiang,Zhang Jiarui,Zhang Xiaowang,Feng Zhiyong

Publisher

Springer Nature Singapore

Reference26 articles.

1. Ahmad, W.U., Chakraborty, S., Ray, B., Chang, K.: A transformer-based approach for source code summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5–10, 2020, pp. 4998–5007. Association for Computational Linguistics (2020)

2. Ahmad, W.U., Chakraborty, S., Ray, B., Chang, K.: Unified pre-training for program understanding and generation. CoRR abs/2103.06333

3. Allamanis, M., Peng, H., Sutton, C.: A convolutional attention network for extreme summarization of source code. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19–24, 2016. JMLR Workshop and Conference Proceedings, vol. 48, pp. 2091–2100. JMLR.org

4. Dathathri, S., et al.: Plug and play language models: a simple approach to controlled text generation. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26–30, 2020. OpenReview.net

5. Feng, Z., et al.: Codebert: a pre-trained model for programming and natural languages. In: Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16–20 November 2020. Findings of ACL, vol. EMNLP 2020, pp. 1536–1547. Association for Computational Linguistics (2020)

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