25 citations · 25 across the 3 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…
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…
Flexible Table Recognition and Semantic Interpretation System
Marcin Namysl, Alexander M. Esser, Sven Behnke +1
Table extraction is an important but still unsolved problem. In this paper, we introduce a flexible and modular table extraction system. We develop two rule-based algorithms that 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…
Efficient, Lexicon-Free OCR using Deep Learning
Marcin Namysl, Iuliu Konya
Contrary to popular belief, Optical Character Recognition (OCR) remains a challenging problem when text occurs in unconstrained environments, like natural scenes, due to geometrica…