Papers by Wesley De Neve

1 papers
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules? (D18-1)

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Challenge: Character-level features are used in many natural language processing algorithms but little is known about the character-level patterns they learn.
Approach: They extend contextual decomposition technique to convolutional neural networks and bidirectional long-term memory networks to evaluate and compare these models for morphological tagging on three morphology-dependent languages.
Outcome: The proposed models implicitly discover understandable linguistic rules for morphological tagging on three morphology-dependent languages.

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