Papers by Ethan Hart

1 papers
Learning Better Internal Structure of Words for Sequence Labeling (D18-1)

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Challenge: a gap exists between methods for learning representations of sentences and words . authors propose a convolutional neural architecture with no down-sampling for learning words based on character embeddings .
Approach: They propose a funnel-shaped wide convolutional neural architecture with no down-sampling for learning words' internal structure.
Outcome: The proposed model outperforms other character embedding models on six sequence labeling datasets.

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