Unsupervised Neural Hidden Markov Models
In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag in duction. 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.
SPNLP@EMNLP