Graph-Based Semi-Supervised Learning for Natural Language Understanding (D19-53)
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| Challenge: | Semi-supervised learning is an efficient method to augment training data from unlabeled data. |
| Approach: | They propose semi-supervised learning models and their inductive variants for NLU and use them to find similar utterances and construct a graph. |
| Outcome: | The proposed model improves the error rate of the model by 5% using publicly available NLU data and models. |
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