NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
This paper presents a new participatory Python-based natural language augmentation framework that supports the creation of transformations (modifications to the data) and filters (data splits according to specific features).
The current version of the framework contains 117 transformations and 23 filters for a variety of natural language tasks.
The authors demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models.
Paper: https://arxiv.org/abs/2112.02721
Code: https://github.com/GEM-benchmark/NL-Augmenter
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-nlaugmenter
#deeplearning #nlp #augmentation #robustness
Team
@data_enthusiasts
@OpenArchiveBooks
This paper presents a new participatory Python-based natural language augmentation framework that supports the creation of transformations (modifications to the data) and filters (data splits according to specific features).
The current version of the framework contains 117 transformations and 23 filters for a variety of natural language tasks.
The authors demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models.
Paper: https://arxiv.org/abs/2112.02721
Code: https://github.com/GEM-benchmark/NL-Augmenter
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-nlaugmenter
#deeplearning #nlp #augmentation #robustness
Team
@data_enthusiasts
@OpenArchiveBooks
