25 citations · 27 across the 4 of their papers we have counts for
4 papers
Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking
Hong Jin Kang, Fabrice Harel-Canada, Muhammad Ali Gulzar +2
Data augmentation techniques apply transformations to existing texts to generate additional data. The transformations may produce low-quality texts, where the meaning of the text i…
Sibylvariant Transformations for Robust Text Classification
Fabrice Harel-Canada, Muhammad Ali Gulzar, Nanyun Peng +1
The vast majority of text transformation techniques in NLP are inherently limited in their ability to expand input space coverage due to an implicit constraint to preserve the orig…
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…
Is Q-Learning Provably Efficient? An Extended Analysis
Kushagra Rastogi, Jonathan Lee, Fabrice Harel-Canada +1
This work extends the analysis of the theoretical results presented within the paper Is Q-Learning Provably Efficient? by Jin et al. We include a survey of related research to cont…