Unsupervised Neural Hidden Markov Models

Ke Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, Kevin Knight

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.

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