Abstract
In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag induction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though with a simpler model easily extended to include additional context.
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CITATION STYLE
Tran, K., Bisk, Y., Vaswani, A., Marcu, D., & Knight, K. (2016). Unsupervised neural hidden Markov models. In Proceedings of the Workshop on Structured Prediction for Natural Language Processing, NLP 2016 at the Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 (pp. 63–71). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-5907
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