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

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

29 Jul 2019arXiv:1907.12412archive 2025-07-28

Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, Haifeng Wang

Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing. Current pre-training procedures usually focus on training the model with several simple tasks to grasp the co-occurrence of words or sentences. However, besides co-occurring, there exists other valuable lexical, syntactic and semantic information in training corpora, such as named entity, semantic closeness and discourse relations. In order to extract to the fullest extent, the lexical, syntactic and semantic information from training corpora, we propose a continual pre-training framework named ERNIE 2.0 which builds and learns incrementally pre-training tasks through constant multi-task learning. Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese. The source codes and pre-trained models have been released at https://github.com/PaddlePaddle/ERNIE.

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Tasks

Chinese Named Entity RecognitionChinese Reading ComprehensionChinese Sentence Pair ClassificationChinese Sentiment AnalysisLinguistic AcceptabilityMulti-Task LearningNamed Entity Recognition (NER)Natural Language InferenceOpen-Domain Question AnsweringQuestion AnsweringSemantic Textual SimilaritySentiment Analysis

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Chinese Named Entity Recognition MSRA ERNIE 2.0 Large F1 95 #10 of 21 Archive leaderboard report
Chinese Named Entity Recognition MSRA ERNIE 2.0 Base F1 93.8 #15 of 21 Archive leaderboard report
Chinese Named Entity Recognition MSRA Dev ERNIE 2.0 Large F1 96.3 #1 of 3 Archive leaderboard report
Chinese Named Entity Recognition MSRA Dev ERNIE 2.0 Base F1 95.2 #2 of 3 Archive leaderboard report
Linguistic Acceptability CoLA ERNIE 2.0 Large Accuracy 63.5% #27 of 43 Archive leaderboard report
Linguistic Acceptability CoLA ERNIE 2.0 Base Accuracy 55.2% #34 of 43 Archive leaderboard report
Natural Language Inference MultiNLI ERNIE 2.0 Large Matched 88.7 #15 of 67 Archive leaderboard report
Natural Language Inference MultiNLI ERNIE 2.0 Large Mismatched 88.8 #15 of 67 Archive leaderboard report
Natural Language Inference MultiNLI ERNIE 2.0 Base Matched 86.1 #28 of 67 Archive leaderboard report
Natural Language Inference MultiNLI ERNIE 2.0 Base Mismatched 85.5 #28 of 67 Archive leaderboard report
Natural Language Inference QNLI ERNIE 2.0 Large Accuracy 94.6% #14 of 43 Archive leaderboard report
Natural Language Inference QNLI ERNIE 2.0 Base Accuracy 92.9% #24 of 43 Archive leaderboard report
Natural Language Inference RTE ERNIE 2.0 Large Accuracy 80.2% #35 of 90 Archive leaderboard report
Natural Language Inference RTE ERNIE 2.0 Base Accuracy 74.8% #45 of 90 Archive leaderboard report
Natural Language Inference WNLI ERNIE 2.0 Large Accuracy 67.8 #19 of 23 Archive leaderboard report
Natural Language Inference XNLI Chinese ERNIE 2.0 Large Accuracy 81 #1 of 3 Archive leaderboard report
Natural Language Inference XNLI Chinese ERNIE 2.0 Base Accuracy 79.7 #2 of 3 Archive leaderboard report
Natural Language Inference XNLI Chinese Dev ERNIE 2.0 Large Accuracy 82.6 #1 of 3 Archive leaderboard report
Natural Language Inference XNLI Chinese Dev ERNIE 2.0 Base Accuracy 81.2 #2 of 3 Archive leaderboard report
Open-Domain Question Answering DuReader ERNIE 2.0 Large EM 64.2 #1 of 2 Archive leaderboard report
Open-Domain Question Answering DuReader ERNIE 2.0 Base EM 61.3 #2 of 2 Archive leaderboard report
Question Answering Quora Question Pairs ERNIE 2.0 Large Accuracy 90.1% #7 of 19 Archive leaderboard report
Question Answering Quora Question Pairs ERNIE 2.0 Base Accuracy 89.8% #10 of 19 Archive leaderboard report
Semantic Textual Similarity MRPC ERNIE 2.0 Large Accuracy 87.4% #28 of 45 Archive leaderboard report
Semantic Textual Similarity MRPC ERNIE 2.0 Base Accuracy 86.1% #34 of 45 Archive leaderboard report
Semantic Textual Similarity STS Benchmark ERNIE 2.0 Large Pearson Correlation 0.912 #12 of 66 Archive leaderboard report
Semantic Textual Similarity STS Benchmark ERNIE 2.0 Base Pearson Correlation 0.876 #23 of 66 Archive leaderboard report
Sentiment Analysis SST-2 Binary classification ERNIE 2.0 Base Accuracy 95 #25 of 87 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AdamAttentionAttention DropoutBERTBPEDense ConnectionsDropoutERNIELayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSentencePieceSoftmaxWeight DecayWordPieceXLNet

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