Revisiting Pre-Trained Models for Chinese Natural Language Processing
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Abstract (arXiv)
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models. In this paper, we target on revisiting Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pre-trained language model series to the community. We also propose a simple but effective model called MacBERT, which improves upon RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac). We carried out extensive experiments on eight Chinese NLP tasks to revisit the existing pre-trained language models as well as the proposed MacBERT. Experimental results show that MacBERT could achieve state-of-the-art performances on many NLP tasks, and we also ablate details with several findings that may help future research. Resources available: https://github.com/ymcui/MacBERT
Key facts. Cited by 266 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Apr 2020.
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Citing papers per year: 2021: 43; 2022: 67; 2023: 64; 2024: 39; 2025: 26; 2026: 27.
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- 2026IndustryLLM: Failure-Driven LLM Training for Industrial Procurement25 Sep 2026
- 2026POSPAN: Position-Constrained Span Masking for Language Model Pre-training13 Sep 2026
- 2026Fine-Grained Visual Preprocessing and Dual-Stream Temporal Modeling for Multimodal Sentiment Analysis on Social Media7 Sep 2026
- 2026Cross-Dataset Stability of Expert-Informed Skill Prompting and Fine-Tuning for Chinese Metaphor Identification26 Aug 2026
- 2026CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph29 Jun 2026
- 2026Explanation-Guided Medical Named Entity Recognition with Stability and Boundary Awareness for Atopic Dermatitis22 Jun 2026
- 2026ASTAR: Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora19 Jun 2026
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