Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing

• URL: http://arxiv.org/abs/2212.07223v1
• Date: Wed, 14 Dec 2022 13:48:32 GMT
• Title: Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing
• Authors: Massimo Nicosia and Francesco Piccinno
• Abstract summary: 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 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.
• Score: 3.431659287330068
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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