Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction (N19-1)
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| Challenge: | Using stochastic gradient descent, part-of-speech (POS) induction is a challenging task. |
| Approach: | They propose to maximize mutual information between the induced label and its context by maximizing mutual information. |
| Outcome: | The proposed approach achieves strong performance on a multitude of datasets and languages with a simple architecture that encodes morphology and context. |
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| Challenge: | a limited set of translations into one or more high-resource languages are available for POS tagging . a bi-LSTM architecture that uses contextualized word embeddings improves performance . |
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Géraldine Damnati, Jeremy Auguste, Alexis Nasr, Delphine Charlet, Johannes Heinecke, Frédéric Béchet
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