Abstract
Pretrained transformer-based language models have produced state-of-the-art performance in most natural language understanding tasks. These models undergo two stages of training: pretraining on a huge corpus of data and fine-tuning on a specific downstream task. The pretraining phase is extremely compute-intensive and requires several high-performance computing devices like GPUs and several days or even months of training, but it is crucial for the model to capture global knowledge and also has a significant impact on the fine-tuning task. This is a major roadblock for researchers without access to sophisticated computing resources. To overcome this challenge, we propose two novel hybrid architectures called HybridBERT (HBERT), which combine self-attention and additive attention mechanisms together with sub-layer normalization. We introduce a computing budget to the pretraining phase, limiting the training time and usage to a single GPU. We show that HBERT attains twice the pretraining accuracy of a vanilla-BERT baseline. We also evaluate our proposed models on two downstream tasks, where we outperform BERT-base while accelerating inference. Moreover, we study the effect of weight initialization with a limited pretraining budget. The code and models are publicly available at: www.github.com/gokulsg/HBERT/.- Anthology ID:
- 2024.naacl-srw.30
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Yang (Trista) Cao, Isabel Papadimitriou, Anaelia Ovalle, Marcos Zampieri, Francis Ferraro, Swabha Swayamdipta
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 285–291
- Language:
- URL:
- https://aclanthology.org/2024.naacl-srw.30
- DOI:
- 10.18653/v1/2024.naacl-srw.30
- Cite (ACL):
- Gokul Srinivasagan and Simon Ostermann. 2024. HybridBERT - Making BERT Pretraining More Efficient Through Hybrid Mixture of Attention Mechanisms. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 285–291, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- HybridBERT - Making BERT Pretraining More Efficient Through Hybrid Mixture of Attention Mechanisms (Srinivasagan & Ostermann, NAACL 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.naacl-srw.30.pdf