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. 
Ke Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, Kevin Knight
cs.CL cs.LG