Papers with TextRNN

2 papers
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit (P19-3)

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Challenge: NeuralClassifier is a toolkit for hierarchical multi-label text classification.
Approach: They propose a toolkit for neural hierarchical multi-label text classification . they use a variety of text encoders to implement the model .
Outcome: The proposed model achieves comparable performance with reported results in the literature.
Interpreting the Robustness of Neural NLP Models to Textual Perturbations (2022.findings-acl)

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Challenge: Modern Natural Language Processing models are sensitive to input perturbations and their performance can decrease when applied to noisy data.
Approach: They propose to explain the extent to which a model is affected by an unseen textual perturbation by the learnability of the perturbation.
Outcome: The proposed model is better at identifying a perturbation (higher learnability) but worse at ignoring it (lower robustness).

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