The DOI matches its Crossref record; 61 of the 66 references it cites were found in a registry or an index, and 1 are not checked yet.
A Systematic Assessment of Syntactic Generalization in Neural Language Models
Open accessAbstract
While state-of-the-art neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broad-coverage predictive performance leads to human-like syntactic knowledge.Furthermore, existing work has not provided a clear picture about the model properties required to produce proper syntactic generalizations.We present a systematic evaluation of the syntactic knowledge of neural language models, testing 20 combinations of model types and data sizes on a set of 34 English-language syntactic test suites.We find substantial differences in syntactic generalization performance by model architecture, with sequential models underperforming other architectures.Factorially manipulating model architecture and training dataset size (1M-40M words), we find that variability in syntactic generalization performance is substantially greater by architecture than by dataset size for the corpora tested in our experiments.Our results also reveal a dissociation between perplexity and syntactic generalization performance.
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“It is evaluated as the model with outperforming bilingual evaluation understudy (BLEU) score among four state-of-art text generation methods (RNN, GAN, GPT, and CTRL) [66] and the best syntactic generalization score among trained (LSTM, ON-LSTM, Transformer, n-gram) and ofthe-shelf (GPT, JRNN, Transformer-XL) models [46].”
From Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023) · cites this paper as [46]
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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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
(2017) · Cited by 11,625
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222
Constraints on variables in syntax
Ross, J. R. (1967) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 4,102
Loper, E., & Bird, S. (2002) · doi:10.3115/1118108.1118117 · Cited by 3,323
Research works printed but not linked: 6
- Andrew Carnie. 2012. Syntax: A generative introduc- tion, volume 18. John Wiley & Sons.
- Anastasia Giannakidou. 2011. Negative and positive polarity items: Variation, licensing, and composi- tionality. In Semantics: An international hand- book of natural language meaning, volume 3, pages 1660-1712. Berlin: Mouton de Gruyter.
- Mario Giulianelli, Jack Harding, Florian Mohnert, Dieuwke Hupkes, and Willem Zuidema. 2018. Un- der the hood: Using diagnostic classifiers to in- vestigate and improve how language models track agreement information. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and In- terpreting Neural Networks for NLP, pages 240- 248.
- Tanya Reinhart. 1981. Definite NP anaphora and c- command domains. Linguistic Inquiry, 12(4):605- 635.
- Ethan Wilcox, Jon Gauthier, Jennifer Hu, Peng Qian, and Roger P. Levy. 2020. Evaluating neural net- works as models of human online language process- ing. In Proceedings of the 42nd Meeting of the Cog- nitive Science Society (CogSci 2020). To appear.
- Ethan Wilcox, Peng Qian, Richard Futrell, Miguel Ballestros, and Roger P. Levy. 2019b. Structural su- pervision improves learning of non-local grammati- cal dependencies. In Proceedings of the 2019 Con- ference of the North American Chapter of the Asso- ciation for Computational Linguistics: Human Lan- guage Technologies, pages 3302-3312, Minneapo- lis, Minnesota.
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The neural architecture of language: Integrative modeling converges on predictive processing
Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105646118 · Cited by 517
Zhou, J., Zhang, Y., Luo, Q., Parker, A. G., & De Choudhury, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581318 · Cited by 288
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality
Thrush, T., Jiang, R., Bartolo, M., Singh, A., Williams, A., Kiela, D., & Ross, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00517 · Cited by 296
What Artificial Neural Networks Can Tell Us about Human Language Acquisition
Warstadt, A., & Bowman, S. R. (2022) · Algebraic Structures in Natural Language · doi:10.1201/9781003205388-2 · Cited by 111
Metadata
- Authors
- Hu, J., Gauthier, J., Qian, P., Wilcox, E., & Levy, R.
- Affiliations
- Massachusetts Institute of Technology (US)
- Harvard University (US)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 1725-1744
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3034510440
- Cited by
- 134
- References
- 66
- Citations by year
- 2026: 11 · 2025: 10 · 2024: 17 · 2023: 40 · 2022: 26 · 2021: 25 · 2020: 5
- FWCI
- 9.17
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Science Foundation
- Open Philanthropy Project
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- syntactic generalization, neural language models, syntactic knowledge, model architecture, training data size, perplexity, English syntax, sequential model, language model evaluation
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