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Unsupervised Neural Hidden Markov Models

2016-09-28WS 2016Code Available0· sign in to hype

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

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Abstract

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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