25 citations · 33 across the 2 of their papers we have counts for
3 papers
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning
Thomas Dopierre, Christophe Gravier, Wilfried Logerais
Recent research considers few-shot intent detection as a meta-learning problem: the model is learning to learn from a consecutive set of small tasks named episodes. In this work, w…
A Neural Few-Shot Text Classification Reality Check
Thomas Dopierre, Christophe Gravier, Wilfried Logerais
Modern classification models tend to struggle when the amount of annotated data is scarce. To overcome this issue, several neural few-shot classification models have emerged, yield…