Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling

Marcin Namysl, Sven Behnke, Joachim Köhler


Anthology ID:
2021.findings-acl.27
Volume:
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Month:
August
Year:
2021
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
314–329
Language:
URL:
https://aclanthology.org/2021.findings-acl.27
DOI:
10.18653/v1/2021.findings-acl.27
Bibkey:
Cite (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.
Cite (Informal):
Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling (Namysl et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-acl.27.pdf
Optional supplementary material:
 2021.findings-acl.27.OptionalSupplementaryMaterial.zip
Video:
 https://aclanthology.org/2021.findings-acl.27.mp4
Code
 mnamysl/nat-acl2021
Data
CoNLL-2003