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 \emph{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.
@article{arxiv.2010.12854,
title = {ReadOnce Transformers: Reusable Representations of Text for Transformers},
author = {Shih-Ting Lin and Ashish Sabharwal and Tushar Khot},
journal= {arXiv preprint arXiv:2010.12854},
year = {2021}
}