Phonotactic learning with neural language models

Connor Mayer & Max Nelson
Computational models of phonotactics share much in common with language models, which assign probabilities to sequences of words. While state of the art language models are implemented using neural networks, phonotactic models have not followed suit. We present several neural models of phonotactics, and show that they perform favorably when compared to existing models. In addition, they provide useful insights into the role of representations on phonotactic learning and generalization. This work provides a promising...

Registration Year

  • 2020
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Resource Types

  • Conference Paper
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Affiliations

  • University of California, Los Angeles
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