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Token free approaches have been successfully applied to a series of word andspan level tasks. In this work, we compare a byte-level (ByT5) and a wordpiecebased (mT5) sequence to sequence model on the 51 languages of the MASSIVEmultilingual semantic parsing dataset. We examine multiple experimentalsettings: (i) zero-shot, (ii) full gold data and (iii) zero-shot with syntheticdata. By leveraging a state-of-the-art label projection method for machinetranslated examples, we are able to reduce the gap in exact match accuracy toonly 5 points with respect to a model trained on gold data from all thelanguages. We additionally provide insights on the cross-lingual transfer ofByT5 and show how the model compares with respect to mT5 across all parametersizes.

Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing