A Systematic Assessment of Syntactic Generalization in Neural Language Models

• URL: http://arxiv.org/abs/2005.03692v2
• Date: Sat, 23 May 2020 02:23:41 GMT
• Title: A Systematic Assessment of Syntactic Generalization in Neural Language Models
• Authors: Jennifer Hu, Jon Gauthier, Peng Qian, Ethan Wilcox, Roger P. Levy
• Abstract summary: We present a systematic evaluation of the syntactic knowledge of neural language models. We find substantial differences in syntactic generalization performance by model architecture. Our results also reveal a dissociation between perplexity and syntactic generalization performance.
• Score: 20.589737524626745
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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