Scaling Hidden Markov Language Models

Justin Chiu, Alexander Rush


Abstract
The hidden Markov model (HMM) is a fundamental tool for sequence modeling that cleanly separates the hidden state from the emission structure. However, this separation makes it difficult to fit HMMs to large datasets in modern NLP, and they have fallen out of use due to very poor performance compared to fully observed models. This work revisits the challenge of scaling HMMs to language modeling datasets, taking ideas from recent approaches to neural modeling. We propose methods for scaling HMMs to massive state spaces while maintaining efficient exact inference, a compact parameterization, and effective regularization. Experiments show that this approach leads to models that are much more accurate than previous HMMs and n-gram-based methods, making progress towards the performance of state-of-the-art NN models.
Anthology ID:
2020.emnlp-main.103
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1341–1349
Language:
URL:
https://aclanthology.org/2020.emnlp-main.103
DOI:
10.18653/v1/2020.emnlp-main.103
Bibkey:
Cite (ACL):
Justin Chiu and Alexander Rush. 2020. Scaling Hidden Markov Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1341–1349, Online. Association for Computational Linguistics.
Cite (Informal):
Scaling Hidden Markov Language Models (Chiu & Rush, EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.103.pdf
Video:
 https://slideslive.com/38940160
Code
 harvardnlp/hmm-lm
Data
Penn Treebank