Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning
Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.
The authors suggest several practical ideas to improving model performance on this task:
- using augmentations (synonym replacement, random insertion, random swap, random deletion) together with triplet loss
- using curriculum learning (two-stage and gradual)
Paper: https://arxiv.org/abs/2103.07552
Code: https://github.com/jasonwei20/triplet-loss
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-nlptriplettricks
#deeplearning #nlp #fewshotlearning #augmentation #curriculumlreaning
Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.
The authors suggest several practical ideas to improving model performance on this task:
- using augmentations (synonym replacement, random insertion, random swap, random deletion) together with triplet loss
- using curriculum learning (two-stage and gradual)
Paper: https://arxiv.org/abs/2103.07552
Code: https://github.com/jasonwei20/triplet-loss
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-nlptriplettricks
#deeplearning #nlp #fewshotlearning #augmentation #curriculumlreaning
