The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Learning a Neural Semantic Parser from User Feedback
Open accessAbstract
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, Luke Zettlemoyer. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.
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Cites
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
Language to Logical Form with Neural Attention
Dong, L., & Lapata, M. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1004 · Cited by 700
Data Recombination for Neural Semantic Parsing
Jia, R., & Liang, P. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1002 · Cited by 441
Latent Predictor Networks for Code Generation
Ling, W., Blunsom, P., Grefenstette, E., Hermann, K. M., Kočiský, T., Wang, F., & Senior, A. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1057 · Cited by 307
Learning Language Games through Interaction
Wang, S. I., Liang, P., & Manning, C. D. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1224 · Cited by 132
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
The Rise and Potential of Large Language Model Based Agents: A Survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2023) · arXiv · doi:10.48550/arxiv.2309.07864 · Cited by 972
The rise and potential of large language model based agents: a survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4222-0 · Cited by 970
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Zhong, V., Xiong, C., & Socher, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.00103 · Cited by 733
Construction of the Literature Graph in Semantic Scholar
Ammar, W., Groeneveld, D., Bhagavatula, C., Beltagy, I., Crawford, M., Downey, D., Dunkelberger, J., Elgohary, A., Feldman, S., Ha, V., et al. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers) · doi:10.18653/v1/n18-3011 · Cited by 358
Metadata
- Authors
- Iyer, S., Konstas, I., Cheung, A., Krishnamurthy, J., & Zettlemoyer, L.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 963-973
- OpenAlex
- W2611818442
- Cited by
- 330
- References
- 6
- FWCI
- 20.45
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- The paper
- Iyer, S., Konstas, I., Cheung, A., Krishnamurthy, J., & Zettlemoyer, L. (2017). Learning a Neural Semantic Parser from User Feedback. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p17-1089
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
