Computer Science > Computation and Language
arXiv:2212.07223 (cs)
[Submitted on 14 Dec 2022]
Title:Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing
View a PDF of the paper titled Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing, by Massimo Nicosia and Francesco Piccinno
View PDFAbstract: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.
| Comments: | Massively Multilingual NLU 2022 Workshop Paper @ EMNLP 2022 - Winning approach of the MMNLU-22 Zero-Shot Challenge |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2212.07223 [cs.CL] |
| (orarXiv:2212.07223v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2212.07223 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing, by Massimo Nicosia and Francesco Piccinno
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