Unsupervised Neural Hidden Markov Models

Understand

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.