65 citations · 103 across the 5 of their papers we have counts for
5 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…
Training Cross-Lingual embeddings for Setswana and Sepedi
Mack Makgatho, Vukosi Marivate, Tshephisho Sefara +1
African languages still lag in the advances of Natural Language Processing techniques, one reason being the lack of representative data, having a technique that can transfer inform…
Low resource language dataset creation, curation and classification: Setswana and Sepedi -- Extended Abstract
Vukosi Marivate, Tshephisho Sefara, Vongani Chabalala +4
The recent advances in Natural Language Processing have only been a boon for well represented languages, negating research in lesser known global languages. This is in part due to…
Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi
Vukosi Marivate, Tshephisho Sefara, Vongani Chabalala +4
The recent advances in Natural Language Processing have been a boon for well-represented languages in terms of available curated data and research resources. One of the challenges…
Improving short text classification through global augmentation methods
Vukosi Marivate, Tshephisho Sefara
We study the effect of different approaches to text augmentation. To do this we use 3 datasets that include social media and formal text in the form of news articles. Our goal is t…