命名實體識別：結合預訓練模型及對抗訓練的解決方案

CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training

本研究使用「ROCLING 2022中文健康照護命名實體辨識任務」(Lee et al., 2022)中的訓練與驗證資料來進行建模。建模過程採用了資料擴增與資料後處理等技術，並使用MacBERT預訓練模型來建立一個專用中文醫療領域的NER辨識器。在微調過程中，我們也加入對抗式訓練的方法，如FGM和PGD，最後調適所得的模型成效接近於任務評測最佳團隊。此外，藉由引入混合精度訓練，我們也大幅降低了訓練所需時間成本。

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 ∗These authors contributed equally to this work. 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.