23 citations · 38 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
Georg Rehm, Dimitrios Galanis, Penny Labropoulou +21
With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composi…
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Georg Rehm, Katrin Marheinecke, Stefanie Hegele +44
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cr…