Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)
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| Challenge: | Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available. |
| Approach: | They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN. |
| Outcome: | The proposed approach significantly improves learning effectiveness when a small number of training examples are available. |
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| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
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Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks (2020.emnlp-main)
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| Challenge: | Neural networks typically need large labeled data for training and are not easily interpretable. |
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| Challenge: | Existing deep learning models for Relation Extraction (RE) have limited generalization beyond the syntactic structures of the input sentences. |
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| Challenge: | Existing methods to integrate neural networks and symbolic rules have their merits and weaknesses. |
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Sketch-Driven Regular Expression Generation from Natural Language and Examples (2020.tacl-1)
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| Challenge: | Existing approaches to debunk false features in deep NLP models are inadequate . previous work suggests that models learn spurious features instead of the true signals of the task . |
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