CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training
Journal
ROCLING 2022 - Proceedings of the 34th Conference on Computational Linguistics and Speech Processing
ISBN
9789869576956
Date Issued
2022-01-01
Author(s)
Abstract
This study uses training and validation data from the”ROCLING 2022 Chinese Health Care Named Entity Recognition Task” for modeling. The modeling process adopts technologies such as data augmentation and data post-processing, and uses the MacBERT pre-training model to build a dedicated Chinese medical field NER recognizer. During the fine-tuning process, we also added adversarial training methods, such as FGM and PGD, and the results of the final tuned model were close to the best team for task evaluation. In addition, by introducing mixed-precision training, we also greatly reduce the time cost of training.
Subjects
Adversarial Training | Conditional Random Field | MacBERT | Name Entity Recognition
Type
conference paper