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arXiv · 2112.07916

LongT5: Efficient Text-To-Text Transformer for Long Sequences

Mandy Guo·Joshua Ainslie·David Uthus·Santiago Ontanon·Jianmo Ni·Yun-Hsuan Sung·Yinfei Yang
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Abstract

Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with which we explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated attention ideas from long-input transformers (ETC), and adopted pre-training strategies from summarization pre-training (PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call {\em Transient Global} (TGlobal), which mimics ETC's local/global attention mechanism, but without requiring additional side-inputs. We are able to achieve state-of-the-art results on several summarization tasks and outperform the original T5 models on question answering tasks.

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Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang. 2021-12-15. LongT5: Efficient Text-To-Text Transformer for Long Sequences. https://arxiv.org/abs/2112.07916

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