ERNIE is a novel language representation model enhanced by knowledge, designed to learn language representation through knowledge masking strategies, including entity-level and phrase-level masking. Inspired by BERT's masking strategy, ERNIE improves language representation by integrating knowledge. Experimental results show that ERNIE outperforms other baseline methods, achieving new state-of-the-art results on five Chinese natural language processing tasks, including natural language inference, semantic similarity, named entity recognition, sentiment analysis, and question answering. ERNIE also demonstrates stronger knowledge inference capacity on a cloze test. The paper introduces ERNIE, which uses knowledge masking strategies to implicitly learn both syntactic and semantic information from phrases and entities. ERNIE significantly outperforms previous state-of-the-art methods on various Chinese natural language processing tasks. The code and pre-trained models of ERNIE are available at https://github.com/PaddlePaddle/LARK/tree/develop/ERNIE. ERNIE is pre-trained on heterogeneous Chinese data and applied to five Chinese NLP tasks. It advances the state-of-the-art results on all these tasks. An additional experiment on the cloze test shows that ERNIE has better knowledge inference capacity over other strong baseline methods. The paper also discusses related work, including context-independent and context-aware representations, and heterogeneous data. ERNIE uses a transformer encoder and integrates knowledge through multi-stage masking strategies. The model is evaluated on five Chinese NLP tasks, showing that ERNIE outperforms BERT on all tasks, achieving new state-of-the-art results. Ablation studies show that knowledge masking strategies and the DLM task contribute to ERNIE's performance. The cloze test results show that ERNIE performs better in context-based knowledge reasoning. The paper concludes that integrating knowledge into pre-training language models improves language representation, and future work will integrate other types of knowledge into semantic representation models.ERNIE is a novel language representation model enhanced by knowledge, designed to learn language representation through knowledge masking strategies, including entity-level and phrase-level masking. Inspired by BERT's masking strategy, ERNIE improves language representation by integrating knowledge. Experimental results show that ERNIE outperforms other baseline methods, achieving new state-of-the-art results on five Chinese natural language processing tasks, including natural language inference, semantic similarity, named entity recognition, sentiment analysis, and question answering. ERNIE also demonstrates stronger knowledge inference capacity on a cloze test. The paper introduces ERNIE, which uses knowledge masking strategies to implicitly learn both syntactic and semantic information from phrases and entities. ERNIE significantly outperforms previous state-of-the-art methods on various Chinese natural language processing tasks. The code and pre-trained models of ERNIE are available at https://github.com/PaddlePaddle/LARK/tree/develop/ERNIE. ERNIE is pre-trained on heterogeneous Chinese data and applied to five Chinese NLP tasks. It advances the state-of-the-art results on all these tasks. An additional experiment on the cloze test shows that ERNIE has better knowledge inference capacity over other strong baseline methods. The paper also discusses related work, including context-independent and context-aware representations, and heterogeneous data. ERNIE uses a transformer encoder and integrates knowledge through multi-stage masking strategies. The model is evaluated on five Chinese NLP tasks, showing that ERNIE outperforms BERT on all tasks, achieving new state-of-the-art results. Ablation studies show that knowledge masking strategies and the DLM task contribute to ERNIE's performance. The cloze test results show that ERNIE performs better in context-based knowledge reasoning. The paper concludes that integrating knowledge into pre-training language models improves language representation, and future work will integrate other types of knowledge into semantic representation models.