The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
Open accessAbstract
Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds of millions of parameters.However, these models suffer from heavy model sizes and high latency such that they cannot be deployed to resourcelimited mobile devices.In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model.Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to various downstream NLP tasks via simple fine-tuning.Basically, MobileBERT is a thin version of BERT LARGE , while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.To train MobileBERT, we first train a specially designed teacher model, an invertedbottleneck incorporated BERT LARGE model.Then, we conduct knowledge transfer from this teacher to MobileBERT.Empirical studies show that MobileBERT is 4.3× smaller and 5.5× faster than BERT BASE while achieving competitive results on well-known benchmarks.On the natural language inference tasks of GLUE, MobileBERT achieves a GLUE score of 77.7 (0.6 lower than BERT BASE ), and 62 ms latency on a Pixel 4 phone.On the SQuAD v1.1/v2.0question answering task, MobileBERT achieves a dev F1 score of 90.0/79.2(1.5/2.1 higher than BERT BASE ).
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“To train MobileBERT, first the authors trained a specially designed teacher model, and then, they transferred the knowledge from the teacher to MobileBERT, which is 4.3× smaller and 5.5× faster than the general BERT model.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [57]
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“The distilled MobileBERT model is 4.3 × smaller than the BERT-base network and 5.5 × faster on a Pixel 4 mobile phone.”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [67]
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“MobileBERT [67] is a compressed version of the BERT-base model, and it runs 5.5 ×”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [67]
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“Moreover, models such as MobileBERT and SqueezeBERT employ bottleneck layers and grouped convolutions, leading to a 3× to 4× reduction in latency while maintaining >90% of the original model’s accuracy.”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [68]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
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Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Research works printed but not linked: 2
- William B Dolan and Chris. Brockett. 2005. Auto- matically constructing a corpus of sentential para- phrases. In Proceedings of the International Work- shop on Paraphrasing. 7 https://tensorflow.org/lite/models/ bert_qa/overview
- Ganesh Jawahar, Benoît Sagot, Djamé Seddah, Samuel Unicomb, Gerardo Iñiguez, Márton Karsai, Yannick Léo, Márton Karsai, Carlos Sarraute, Éric Fleury, et al. 2019. What does bert learn about the structure of language? In 57th Annual Meeting of the Associa- tion for Computational Linguistics (ACL), Florence, Italy.
Cited by
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Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
A Metaverse: Taxonomy, Components, Applications, and Open Challenges
Park, S., & Kim, Y. (2022) · IEEE Access · doi:10.1109/access.2021.3140175 · Cited by 2,046
TinyBERT: Distilling BERT for natural language understanding
Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L., Wang, F., & Liu, Q. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.372 · Cited by 1,786
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 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
Metadata
- Authors
- Sun, Z., Yu, H., Song, X., Liu, R., Yang, Y., & Zhou, D.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 2158-2170
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3034457371
- Cited by
- 757
- References
- 54
- Citations by year
- 2026: 94 · 2025: 134 · 2024: 98 · 2023: 128 · 2022: 119 · 2021: 140 · 2020: 43
- FWCI
- 41.07
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- MobileBERT, BERT, model compression, knowledge distillation, teacher-student learning, self-attention, feedforward neural networks, mobile devices, resource-constrained deployment, natural language inference, question answering, adhesives, SQuAD
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