Design Challenges and Misconceptions in Neural Sequence Labeling

Jie Yang, Shuailong Liang, Yue Zhang


Abstract
We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.
Anthology ID:
C18-1327
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3879–3889
Language:
URL:
https://aclanthology.org/C18-1327
DOI:
Bibkey:
Cite (ACL):
Jie Yang, Shuailong Liang, and Yue Zhang. 2018. Design Challenges and Misconceptions in Neural Sequence Labeling. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3879–3889, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Design Challenges and Misconceptions in Neural Sequence Labeling (Yang et al., COLING 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/C18-1327.pdf
Code
 jiesutd/NCRFpp +  additional community code
Data
Penn Treebank