The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Abstract Syntax Networks for Code Generation and Semantic Parsing
Open accessAbstract
Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs.We introduce abstract syntax networks, a modeling framework for these problems.The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree.On the benchmark HEARTHSTONE dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-ofthe-art values of 67.1 and 6.1%.Furthermore, we perform competitively on the ATIS, JOBS, and GEO semantic parsing datasets with no task-specific engineering.* Equal contribution.name: [ 'D', 'i', 'r', 'e', ' ', 'W', 'o', 'l', 'f', ' ', 'A', 'l', 'p', 'h', 'a'] cost: ['2'] type: ['Minion'] rarity: ['Common'] race: ['Beast'] class: ['Neutral'] description: [ 'Adjacent', 'minions', 'have', '+', '1', 'Attack', '.'] health: ['2'] attack: ['2'] durability: ['-1'] class DireWolfAlpha(MinionCard): def __init__(self): super().__init__("Dire Wolf Alpha", 2, CHARACTER_CLASS.ALL, CARD_RARITY.COMMON, minion_type=MINION_TYPE.BEAST) def create_minion(self, player): return Minion(2, 2, auras=[ Aura(ChangeAttack(1), MinionSelector(Adjacent())) ])
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Cites
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
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Andreas, J., Rohrbach, M., Darrell, T., & Klein, D. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.12 · Cited by 908
Self-citationZettlemoyer, L., & Collins, M. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.1420 · Cited by 728
Cited by
Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1425 · Cited by 1,034
GraphCodeBERT: Pre-training Code Representations with Data Flow
Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Svyatkovskiy, A., Fu, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.08366 · Cited by 480
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
Ren, S., Guo, D., Lu, S., Zhou, L., Liu, S., Tang, D., Sundaresan, N., Zhou, M., Blanco, A., & Ma, S. (2020) · arXiv · doi:10.48550/arxiv.2009.10297 · Cited by 214
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
Yin, P., & Neubig, G. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/d18-2002 · Cited by 203
Learning to mine aligned code and natural language pairs from stack overflow
Yin, P., Deng, B., Chen, E., Vasilescu, B., & Neubig, G. (2018) · Proceedings of the 15th International Conference on Mining Software Repositories · doi:10.1145/3196398.3196408 · Cited by 218
“.e., garbage in, garbage out [35]. For a particular class of applications, such as source code retrieval given a NL query [42], source code summarization in NL [15], and source code synthesis from NL [33, 47], all of which involve correspondence between NL utterances and code, it is essential to have access to high vo…”
Background
Metadata
- Authors
- Rabinovich, M., Stern, M., & Klein, D.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1139-1149
- OpenAlex
- W2962728167
- Cited by
- 312
- References
- 33
- Type
- conference abstract
- Language
- en
- Open access
- hybrid
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- Rabinovich, M., Stern, M., & Klein, D. (2017). Abstract Syntax Networks for Code Generation and Semantic Parsing. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p17-1105
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