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Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication ...
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A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans ...
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A Systematic Assessment of Syntactic Generalization in Neural Language Models ...
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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 ... : To appear in the Proceedings of the Association for Computational Linguistics (ACL 2020) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2005.03692 https://dx.doi.org/10.48550/arxiv.2005.03692
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