@inproceedings{lee-berg-kirkpatrick-2025-optimizing,
    title = "Optimizing Hidden {M}arkov Language Models: An Empirical Study of Reparameterization and Initialization Techniques",
    author = "Lee, Ivan  and
      Berg-Kirkpatrick, Taylor",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-naacl.429/",
    pages = "7712--7723",
    ISBN = "979-8-89176-195-7",
    abstract = "Hidden Markov models (HMMs) are valuable for their ability to provide exact and tractable inference. However, learning an HMM in an unsupervised manner involves a non-convex optimization problem that is plagued by poor local optima. Recent work on scaling-up HMMs to perform competitively as language models has indicated that this challenge only increases with larger hidden state sizes. Several techniques to address this problem have been proposed, but have not be evaluated comprehensively. This study provides a comprehensive empirical analysis of two recent strategies that use neural networks to enhance HMM optimization: neural reparameterization and neural initialization. We find that (1) these techniques work effectively for scaled HMM language modeling, (2) linear reparameterizations can be as effective as non-linear ones, and (3) the strategies are complementary."
}