Abstract
We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing on the task-specific parts of an input. A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust trainable top-k operator.Our experiments on a challenging long document summarization task show that even our simple baseline performs comparably to the current SOTA, and with trainable pooling we can retain its top quality, while being 1.8× faster during training, 4.5× faster during inference, and up to 13× more computationally efficient in the decoder.- Anthology ID:
- 2022.acl-long.590
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8616–8633
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.590
- DOI:
- 10.18653/v1/2022.acl-long.590
- Cite (ACL):
- Michał Pietruszka, Łukasz Borchmann, and Łukasz Garncarek. 2022. Sparsifying Transformer Models with Trainable Representation Pooling. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8616–8633, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Sparsifying Transformer Models with Trainable Representation Pooling (Pietruszka et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.acl-long.590.pdf
- Code
- applicaai/pyramidions
- Data
- Pubmed, arXiv Summarization Dataset