Abstract
We introduce EdgeFormer – a parameter-efficient Transformer for on-device seq2seq generation under the strict computation and memory constraints. Compared with the previous parameter-efficient Transformers, EdgeFormer applies two novel principles for cost-effective parameterization, allowing it to perform better given the same parameter budget; moreover, EdgeFormer is further enhanced by layer adaptation innovation that is proposed for improving the network with shared layers.Extensive experiments show EdgeFormer can effectively outperform previous parameter-efficient Transformer baselines and achieve competitive results under both the computation and memory constraints. Given the promising results, we release EdgeLM – the pretrained version of EdgeFormer, which is the first publicly available pretrained on-device seq2seq model that can be easily fine-tuned for seq2seq tasks with strong results, facilitating on-device seq2seq generation in practice.- Anthology ID:
- 2022.emnlp-main.741
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10786–10798
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.741
- DOI:
- 10.18653/v1/2022.emnlp-main.741
- Cite (ACL):
- Tao Ge, Si-Qing Chen, and Furu Wei. 2022. EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10786–10798, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation (Ge et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.emnlp-main.741.pdf