4,216 research outputs found
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.Comment: accepted at EMNLP 2016, Workshop on Structured Prediction for NLP.
Oral presentatio
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors
with overlapping, global features. Each input's latent representation is
predicted conditional on the observable data using a feature-rich conditional
random field. Then a reconstruction of the input is (re)generated, conditional
on the latent structure, using models for which maximum likelihood estimation
has a closed-form. Our autoencoder formulation enables efficient learning
without making unrealistic independence assumptions or restricting the kinds of
features that can be used. We illustrate insightful connections to traditional
autoencoders, posterior regularization and multi-view learning. We show
competitive results with instantiations of the model for two canonical NLP
tasks: part-of-speech induction and bitext word alignment, and show that
training our model can be substantially more efficient than comparable
feature-rich baselines
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