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%0 Conference Proceedings
%T Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling
%A Namysl, Marcin
%A Behnke, Sven
%A Köhler, Joachim
%S Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F namysl-etal-2021-empirical
%R 10.18653/v1/2021.findings-acl.27
%U https://aclanthology.org/2021.findings-acl.27
%U https://doi.org/10.18653/v1/2021.findings-acl.27
%P 314-329

Markdown (Informal)

[Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling](https://aclanthology.org/2021.findings-acl.27) (Namysl et al., Findings 2021)

• Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling (Namysl et al., Findings 2021)

ACL

• Marcin Namysl, Sven Behnke, and Joachim Köhler. 2021. Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 314–329, Online. Association for Computational Linguistics.