The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Pre-Training With Whole Word Masking for Chinese BERT
Open accessAbstract
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and its consecutive variants have been proposed to further improve the performance of the pre-trained language models. In this paper, we aim to first introduce the whole word masking (wwm) strategy for Chinese BERT, along with a series of Chinese pre-trained language models. Then we also propose a simple but effective model called MacBERT, which improves upon RoBERTa in several ways. Especially, we propose a new masking strategy called MLM as correction (Mac). To demonstrate the effectiveness of these models, we create a series of Chinese pre-trained language models as our baselines, including BERT, RoBERTa, ELECTRA, RBT, etc. We carried out extensive experiments on ten Chinese NLP tasks to evaluate the created Chinese 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. We open-source our pre-trained language models for further facilitating our research community.
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Self-citationSQuAD: 100,000+ Questions for Machine Comprehension of Text
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SpanBERT: Improving Pre-training by Representing and Predicting Spans
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Cited by
Pre-trained models for natural language processing: A survey
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1647-3 · Cited by 1,591
A primer in BERTology: What we know about how BERT works
Rogers, A., Kovaleva, O., & Rumshisky, A. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00349 · Cited by 1,552
A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123618 · Cited by 1,452
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
Kim, W., Son, B., & Kim, I. (2021) · arXiv · doi:10.48550/arxiv.2102.03334 · Cited by 951
K-BERT: Enabling Language Representation with Knowledge Graph
Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., & Wang, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5681 · Cited by 894
Metadata
- Authors
- Cui, Y., Che, W., Liu, T., Qin, B., & Yang, Z.
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2021
- Volume
- 29
- Pages
- 3504-3514
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W2952370363
- Cited by
- 1,181
- References
- 13
- FWCI
- 67.22
- Type
- journal article
- Language
- en
- Open access
- green
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