Recurrent Neural Network Grammars
Open accessAbstract
Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, Noah A. Smith. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.
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Despite the lack of task-specific tuning, the Transformer performs better than all previously reported models on English constituency parsing with the exception of the Recurrent Neural Network Grammar [8].
“Our results in Table 4 show that despite the lack of task-specific tuning our model performs surprisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar [8].”
From Attention Is All You Need (2017) · cites this paper as [8]
Cites
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works
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Targeted Syntactic Evaluation of Language Models
Marvin, R., & Linzen, T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1151 · Cited by 334 · see citing works
Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling
Logan, R., Liu, N. F., Peters, M. E., Gardner, M., & Singh, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1598 · Cited by 168 · see citing works
A Systematic Assessment of Syntactic Generalization in Neural Language Models
Hu, J., Gauthier, J., Qian, P., Wilcox, E., & Levy, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.158 · Cited by 134 · see citing works
Syntactic Structure from Deep Learning
Linzen, T., & Baroni, M. (2021) · Annual Review of Linguistics · doi:10.1146/annurev-linguistics-032020-051035 · Cited by 202 · see citing works
Oh, B.-D., & Schuler, W. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00548 · Cited by 107 · see citing works
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., & Simaan, K. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1209 · Cited by 97 · see citing works
McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00567 · Cited by 70 · see citing works
McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2021) · arXiv · doi:10.48550/arxiv.2111.09509 · Cited by 70 · see citing works
Assessing Phrasal Representation and Composition in Transformers
Yu, L., & Ettinger, A. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.397 · Cited by 58 · see citing works
SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
Gauthier, J., Hu, J., Wilcox, E., Qian, P., & Levy, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.acl-demos.10 · Cited by 61 · see citing works
Localizing syntactic predictions using recurrent neural network grammars
Brennan, J. R., Dyer, C., Kuncoro, A., & Hale, J. T. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107479 · Cited by 58 · see citing works
Self-citationShen, Y., Tay, Y., Zheng, C., Bahri, D., Metzler, D., & Courville, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.559 · Cited by 29 · see citing works
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
“(2014) [37] semi-supervised 92.1 Transformer (4 layers) semi-supervised 92.7 Luong et al. (2015) [23] multi-task 93.0 Dyer et al. (2016) [8] generative 93.3 increased the maximum output length to input length + 300 .”
ResultInfluential
- Authors
- Dyer, C., Kuncoro, A., Ballesteros, M., & Smith, N. A.
- Affiliations
- Carnegie Mellon University (US)
- Universitat Pompeu Fabra (ES)
- University of Washington (US)
- Venue
- Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2016
- Pages
- 199-209
- OpenAlex
- W2289899728
- Cited by
- 517
- References
- 1
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 20 · 2023: 31 · 2022: 34 · 2021: 68 · 2020: 100 · 2019: 103 · 2018: 75 · 2017: 51 · 2016: 19
- FWCI
- 44.54
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- U.S. Department of Defense
- European Commission (FP7 ICT, 610411, H2020, 645012)
- Defense Advanced Research Projects Agency (HR0011, HR0011-15-C-0114)
- Advanced Research Projects Agency
- Horizon 2020 Framework Programme
- Army Research Office (W911NF-15-1-, W911NF, W911NF-15)
- FP7 Information and Communication Technologies
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- neural language models, sequence generation
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