Papers with word deletion

2 papers
Comparing Automatic and Human Evaluation of Local Explanations for Text Classification (N18-1)

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Challenge: Text classification models are becoming increasingly complex and opaque, however for many applications it is essential that the models are interpretable.
Approach: They propose to use automatic word deletion to generate local explanations for a text classification model by crowdsourcing the evaluation using a crowdsourced experiment.
Outcome: The proposed evaluations of local explanations using crowdsourcing and automatic measures correlate with the results.
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning (2024.lrec-main)

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Challenge: Existing methods for data augmentation have not been well explored.
Approach: They propose to use punctuation insertion, modal verbs, and double negation to produce diverse forms of sentences.
Outcome: The proposed methods perform better on diverse datasets with semantic similarity and standard negation.

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