- Anthology ID:
- 2021.findings-acl.27
- Volume:
- Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.findings-acl.27.pdf
- Code
- mnamysl/nat-acl2021
- Data
- CoNLL 2003