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We present ReadOnce Transformers, an approach to convert a transformer-basedmodel into one that can build an information-capturing, task-independent, andcompressed representation of text. The resulting representation is reusableacross different examples and tasks, thereby requiring a document shared acrossmany examples or tasks to only be \emph{read once}. This leads to fastertraining and evaluation of models. Additionally, we extend standardtext-to-text transformer models to Representation+Text-to-text models, andevaluate on multiple downstream tasks: multi-hop QA, abstractive QA, andlong-document summarization. Our one-time computed representation results in a2x-5x speedup compared to standard text-to-text models, while the compressionalso allows existing language models to handle longer documents without theneed for designing new pre-trained models.

ReadOnce Transformers: Reusable Representations of Text for Transformers