25 citations · 25 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling
Marcin Namysl, Sven Behnke, Joachim Köhler
Despite recent advances, standard sequence labeling systems often fail when processing noisy user-generated text or consuming the output of an Optical Character Recognition (OCR) p…
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
Marcin Namysl, Sven Behnke, Joachim Köhler
Sequence labeling systems should perform reliably not only under ideal conditions but also with corrupted inputs - as these systems often process user-generated text or follow an e…