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While state-of-the-art neural network models continue to achieve lowerperplexity scores on language modeling benchmarks, it remains unknown whetheroptimizing for broad-coverage predictive performance leads to human-likesyntactic knowledge. Furthermore, existing work has not provided a clearpicture about the model properties required to produce proper syntacticgeneralizations. We present a systematic evaluation of the syntactic knowledgeof neural language models, testing 20 combinations of model types and datasizes on a set of 34 English-language syntactic test suites. We findsubstantial differences in syntactic generalization performance by modelarchitecture, with sequential models underperforming other architectures.Factorially manipulating model architecture and training dataset size (1M--40Mwords), we find that variability in syntactic generalization performance issubstantially greater by architecture than by dataset size for the corporatested in our experiments. Our results also reveal a dissociation betweenperplexity and syntactic generalization performance.

A Systematic Assessment of Syntactic Generalization in Neural Language Models