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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.
* accepted at EMNLP 2016, Workshop on Structured Prediction for NLP.
Oral presentation