Longt5: Efficient Text-to-text Transformer For Long Sequences

Guo Mandy, Ainslie Joshua, Uthus David, Ontanon Santiago, Ni Jianmo, Sung Yun-hsuan, Yang Yinfei. Arxiv 2021

[Paper]    
Applications Attention Mechanism Model Architecture Pretraining Methods Training Techniques Transformer

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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