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        <title>A Systematic Assessment of Syntactic Generalization in Neural Language Models</title>
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        <namePart type="given">Jennifer</namePart>
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        <namePart type="given">Peng</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T A Systematic Assessment of Syntactic Generalization in Neural Language Models
%A Hu, Jennifer
%A Gauthier, Jon
%A Qian, Peng
%A Wilcox, Ethan
%A Levy, Roger
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F hu-etal-2020-systematic
%X 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.
%R 10.18653/v1/2020.acl-main.158
%U https://aclanthology.org/2020.acl-main.158
%U https://doi.org/10.18653/v1/2020.acl-main.158
%P 1725-1744

Markdown (Informal)

[A Systematic Assessment of Syntactic Generalization in Neural Language Models](https://aclanthology.org/2020.acl-main.158) (Hu et al., ACL 2020)

• A Systematic Assessment of Syntactic Generalization in Neural Language Models (Hu et al., ACL 2020)

ACL

• Jennifer Hu, Jon Gauthier, Peng Qian, Ethan Wilcox, and Roger Levy. 2020. A Systematic Assessment of Syntactic Generalization in Neural Language Models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1725–1744, Online. Association for Computational Linguistics.