Papers › Building Chinese Biomedical Language Models via Multi-Level Text Discrimination

Building Chinese Biomedical Language Models via Multi-Level Text Discrimination

14 Oct 2021arXiv:2110.07244archive 2025-07-28

Quan Wang, Songtai Dai, Benfeng Xu, Yajuan Lyu, Yong Zhu, Hua Wu, Haifeng Wang

Pre-trained language models (PLMs), such as BERT and GPT, have revolutionized the field of NLP, not only in the general domain but also in the biomedical domain. Most prior efforts in building biomedical PLMs have resorted simply to domain adaptation and focused mainly on English. In this work we introduce eHealth, a Chinese biomedical PLM built from scratch with a new pre-training framework. This new framework pre-trains eHealth as a discriminator through both token- and sequence-level discrimination. The former is to detect input tokens corrupted by a generator and recover their original identities from plausible candidates, while the latter is to further distinguish corruptions of a same original sequence from those of others. As such, eHealth can learn language semantics at both token and sequence levels. Extensive experiments on 11 Chinese biomedical language understanding tasks of various forms verify the effectiveness and superiority of our approach. We release the pre-trained model at \url{https://github.com/PaddlePaddle/Research/tree/master/KG/eHealth} and will also release the code later.

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Adam Attention Attention Dropout BERT BPE Cosine Annealing Dense Connections Discriminative Fine-Tuning Dropout GPT Layer Normalization Linear Layer Linear Warmup With Cosine Annealing Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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