Mayer & Nelson (2019) – Phonotactic learning with neural language models

Phonotactic learning with neural language models
Connor Mayer, Max Nelson
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October 2019
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 starting point for future modeling of human phonotactic knowledge.

Format: [ pdf ]
Reference: lingbuzz/004834
(please use that when you cite this article)
Published in: Proceedings of the Society for Computation in Linguistics
keywords: phonology, phonotactics, neural networks, sonority sequencing