Abstract
We present ReadOnce Transformers, an approach to convert a transformer-based model into one that can build an information-capturing, task-independent, and compressed representation of text. The resulting representation is reusable across different examples and tasks, thereby requiring a document shared across many examples or tasks to only be read once. This leads to faster training and evaluation of models. Additionally, we extend standard text-to-text transformer models to Representation+Text-to-text models, and evaluate on multiple downstream tasks: multi-hop QA, abstractive QA, and long-document summarization. Our one-time computed representation results in a 2x-5x speedup compared to standard text-to-text models, while the compression also allows existing language models to handle longer documents without the need for designing new pre-trained models.- Anthology ID:
- 2021.acl-long.554
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7129–7141
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.554
- DOI:
- 10.18653/v1/2021.acl-long.554
- Cite (ACL):
- Shih-Ting Lin, Ashish Sabharwal, and Tushar Khot. 2021. ReadOnce Transformers: Reusable Representations of Text for Transformers. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7129–7141, Online. Association for Computational Linguistics.
- Cite (Informal):
- ReadOnce Transformers: Reusable Representations of Text for Transformers (Lin et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.acl-long.554.pdf
- Data
- HotpotQA, NarrativeQA, SQuAD