Token free approaches have been successfully applied to a series of word and
span level tasks. In this work, we compare a byte-level (ByT5) and a wordpiece
based (mT5) sequence to sequence model on the 51 languages of the MASSIVE
multilingual semantic parsing dataset. We examine multiple experimental
settings: (i) zero-shot, (ii) full gold data and (iii) zero-shot with synthetic
data. By leveraging a state-of-the-art label projection method for machine
translated examples, we are able to reduce the gap in exact match accuracy to
only 5 points with respect to a model trained on gold data from all the
languages. We additionally provide insights on the cross-lingual transfer of
ByT5 and show how the model compares with respect to mT5 across all parameter
sizes.

本研究研究并比较了一个基于字节的（ByT5）和基于单词片段的（mT5）序列模型在 51 种语言的 MASSIVE 多语种语义分析数据集上的表现，实验结果表明通过使用最新的标签投影方法，机器翻译示例可以显著提高精确度，与在所有语言的黄金数据上训练的模型只有 5 个百分点的差距，并提供了有关 ByT5 的跨语言转移及其相对于 mT5 在所有参数大小上的各自表现的见解。