Towards No.1 in CLUE Semantic Matching Challenge: Pre-trained Language Model Erlangshen with Propensity-Corrected Loss

• URL: http://arxiv.org/abs/2208.02959v1
• Date: Fri, 5 Aug 2022 02:52:29 GMT
• Title: Towards No.1 in CLUE Semantic Matching Challenge: Pre-trained Language Model Erlangshen with Propensity-Corrected Loss
• Authors: Junjie Wang, Yuxiang Zhang, Ping Yang, Ruyi Gan
• Abstract summary: This report describes a pre-trained language model Erlangshen with propensity-corrected loss. We construct a dynamic masking strategy based on knowledge in Masked Language Modeling (MLM) with whole word masking. Overall, we achieve 72.54 points in F1 Score and 78.90 points in Accuracy on the test set.
• Score: 12.034243662298035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This report describes a pre-trained language model Erlangshen with propensity-corrected loss, the No.1 in CLUE Semantic Matching Challenge. In the pre-training stage, we construct a dynamic masking strategy based on knowledge in Masked Language Modeling (MLM) with whole word masking. Furthermore, by observing the specific structure of the dataset, the pre-trained Erlangshen applies propensity-corrected loss (PCL) in the fine-tuning phase. Overall, we achieve 72.54 points in F1 Score and 78.90 points in Accuracy on the test set. Our code is publicly available at: https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/hf-ds/fengshen/examples/clue_sim.

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