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Recently, pre-trained models have achieved state-of-the-art results invarious language understanding tasks, which indicates that pre-training onlarge-scale corpora may play a crucial role in natural language processing.Current pre-training procedures usually focus on training the model withseveral simple tasks to grasp the co-occurrence of words or sentences. However,besides co-occurring, there exists other valuable lexical, syntactic andsemantic information in training corpora, such as named entity, semanticcloseness and discourse relations. In order to extract to the fullest extent,the lexical, syntactic and semantic information from training corpora, wepropose a continual pre-training framework named ERNIE 2.0 which builds andlearns incrementally pre-training tasks through constant multi-task learning.Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on16 tasks including English tasks on GLUE benchmarks and several common tasks inChinese. The source codes and pre-trained models have been released athttps://github.com/PaddlePaddle/ERNIE.

ERNIE 2.0: A Continual Pre-training Framework for Language Understanding