Fetching the paper…
Reading the bibliography…
When applying the Transformer architecture to source code, designing a good self-attention mechanism is critical as it affects how node relationship is extracted from the Abstract Syntax Trees (ASTs) of the source code.
K. Papineni, S. Roukos, T. Ward, and W. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics ACL , 2002
2002
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” 2004
2004
Earlier work this paper cites.
S. Banerjee and A. Lavie, “METEOR: an automatic metric for MT evaluation with improved correlation with human judgments,” in Proceedings of the Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization@ACL , 2005
2005
Earlier work this paper cites.
S. Haiduc, J. Aponte, and A. Marcus, “Supporting program comprehension with source code summarization,” in Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2, ICSE 2010, Cape Town, South Africa, 1-8 May 2010 , 2010
2010
Earlier work this paper cites.
P. W. McBurney and C. McMillan, “Automatic documentation generation via source code summarization of method context,” in 22nd International Conference on Program Comprehension, ICPC 2014, Hyderabad, India, June 2-3, 2014 , 2014
2014
Earlier work this paper cites.
R. Minelli, A. Mocci, and M. Lanza, “I know what you did last summer - an investigation of how developers spend their time,” in IEEE 23rd International Conference on Program Comprehension (ICPC) , 2015
2015
Earlier work this paper cites.
S. Iyer, I. Konstas, A. Cheung, and L. Zettlemoyer, “Summarizing source code using a neural attention model,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL , 2016
2016
Earlier work this paper cites.
A. Eriguchi, K. Hashimoto, and Y. Tsuruoka, “Tree-to-sequence attentional neural machine translation,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL , 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems NIPS , 2017
2017
Earlier work this paper cites.
S. Jiang, A. Armaly, and C. McMillan, “Automatically generating commit messages from diffs using neural machine translation,” in Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, ASE , 2017
2017
Earlier work this paper cites.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in International Conference on Machine Learning, ICML , 2017
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in 5th International Conference on Learning Representations, ICLR , 2017
2017
Earlier work this paper cites.
P. Shaw, J. Uszkoreit, and A. Vaswani, “Self-attention with relative position representations,” in North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT , 2018
2018
Earlier work this paper cites.
X. Xia, L. Bao, D. Lo, Z. Xing, A. E. Hassan, and S. Li, “Measuring program comprehension: A large-scale field study with professionals,” 2018
2018
Earlier work this paper cites.
X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin, “Deep code comment generation,” in IEEE/ACM 26th International Conference on Program Comprehension (ICPC) , 2018
2018
Earlier work this paper cites.
X. Hu, G. Li, X. Xia, D. Lo, S. Lu, and Z. Jin, “Summarizing source code with transferred API knowledge,” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI , 2018
2018
Earlier work this paper cites.
Y. Wan, Z. Zhao, M. Yang, G. Xu, H. Ying, J. Wu, and P. S. Yu, “Improving automatic source code summarization via deep reinforcement learning,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE , 2018
2018
Cited alongside, same era.
J. Zhang, M. Utiyama, E. Sumita, G. Neubig, and S. Nakamura, “Guiding neural machine translation with retrieved translation pieces,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT , 2018
2018
Cited alongside, same era.
J. Qiu, J. Tang, H. Ma, Y. Dong, K. Wang, and J. Tang, “DeepInf: Social Influence Prediction with Deep Learning,” in International Conference on Knowledge Discovery Data Mining SIGKDD , 2018
2018
Cited alongside, same era.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in 6th International Conference on Learning Representations, ICLR , 2018
X. Chu, B. Zhang, Z. Tian, X. Wei, and H. Xia, “Do we really need explicit position encodings for vision transformers?” 2021
2021
Later among the works it cites.
S. Gao, C. Gao, Y. He, J. Zeng, L. Y. Nie, and X. Xia, “Code structure guided transformer for source code summarization,” ACM Transactions on Software Engineering and Methodology , 2021
2021
Later among the works it cites.
P. He, X. Liu, J. Gao, and W. Chen, “Deberta: decoding-enhanced bert with disentangled attention,” in 9th International Conference on Learning Representations, ICLR , 2021
2021
Later among the works it cites.
Y. Choi, J. Bak, C. Na, and J. Lee, “Learning sequential and structural information for source code summarization,” in Findings of the Association for Computational Linguistics: ACL/IJCNLP , 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
V. L. Shiv and C. Quirk, “Novel positional encodings to enable tree-based transformers,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems NeurIPS , 2019
2019
Cited alongside, same era.
B. Wei, G. Li, X. Xia, Z. Fu, and Z. Jin, “Code generation as a dual task of code summarization,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems NeurIPS , 2019
2019
Cited alongside, same era.
U. Alon, S. Brody, O. Levy, and E. Yahav, “code2seq: Generating sequences from structured representations of code,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 , 2019
2019
Cited alongside, same era.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in 7th International Conference on Learning Representations, ICLR , 2019
2019
Cited alongside, same era.
S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, “Session-based recommendation with graph neural networks,” 2019
2019
Cited alongside, same era.
H. Maron, H. Ben-Hamu, N. Shamir, and Y. Lipman, “Invariant and equivariant graph networks,” in 7th International Conference on Learning Representations, ICLR , 2019
2019
Cited alongside, same era.
T. B. Brown, B. Mann, and N. R. et al., “Language models are few-shot learners,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems NeurIPS , 2020
2020
Cited alongside, same era.
A. Nambiar, M. Heflin, S. Liu, S. Maslov, M. Hopkins, and A. M. Ritz, “Transforming the language of life: Transformer neural networks for protein prediction tasks,” in International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB , 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
H. Peng, G. Li, W. Wang, Y. Zhao, and Z. Jin, “Integrating tree path in transformer for code representation,” in 35th Conference on Neural Information Processing Systems NeurIPS 2021 , 2021
2021
Later among the works it cites.
S. Liu, Y. Chen, X. Xie, J. K. Siow, and Y. Liu, “Retrieval-augmented generation for code summarization via hybrid GNN,” in 9th International Conference on Learning Representations ICLR , 2021
2021
Later among the works it cites.
C. Ying, T. Cai, S. Luo, S. Zheng, G. Ke, D. He, Y. Shen, and T.-Y. Liu, “Do transformers really perform bad for graph representation?” in 35th Conference on Neural Information Processing Systems NeurIPS , 2021
2021
Later among the works it cites.
J. Kim, S. Oh, and S. Hong, “Transformers generalize deepsets and can be extended to graphs & hypergraphs,” in Annual Conference on Neural Information Processing Systems, NeurIPS , 2021
2021
Later among the works it cites.
V. P. Dwivedi, A. T. Luu, T. Laurent, Y. Bengio, and X. Bresson, “Graph neural networks with learnable structural and positional representations,” in The Tenth International Conference on Learning Representations, ICLR , 2022
2022
Later among the works it cites.
Z. Tang, X. Shen, C. Li, J. Ge, L. Huang, Z. Zhu, and B. Luo, “Ast-trans: Code summarization with efficient tree-structured attention,” in IEEE/ACM 44th International Conference on Software Engineering (ICSE) , 2022
2022
Later among the works it cites.
J. Guo, J. Liu, Y. Wan, L. Li, and P. Zhou, “Modeling hierarchical syntax structure with triplet position for source code summarization,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022 , 2022
2022
Later among the works it cites.
S. Cho, S. Min, J. Kim, M. Lee, H. Lee, and S. Hong, “Transformers meet stochastic block models: Attention with data-adaptive sparsity and cost,” in 35th Conference on Neural Information Processing Systems NeurIPS , 2022
2022
Later among the works it cites.
H. Wang, H. Yin, M. Zhang, and P. Li, “Equivariant and stable positional encoding for more powerful graph neural networks,” in The Tenth International Conference on Learning Representations, ICLR , 2022
2022
Later among the works it cites.
J. Kim, T. D. Nguyen, S. Min, S. Cho, M. Lee, H. Lee, and S. Hong, “Pure transformers are powerful graph learners,” in Annual Conference on Neural Information Processing Systems, NeurIPS , 2022
2022
Later among the works it cites.