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2020
- 20.500.13089/1dlc
Ce document est lié à :
https://hdl.handle.net/20.500.13089/1chq
Ce document est lié à :
https://doi.org/10.4000/books.aaccademia
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/isbn/979-12-80136-33-6
info:eu-repo/semantics/openAccess , https://www.openedition.org/12554
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Samuel Louvan et al., « Simple Data Augmentation for Multilingual NLU in Task Oriented Dialogue Systems », Accademia University Press
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Résumé
Data augmentation has shown potential in alleviating data scarcity for Natural Language Understanding (e.g. slot filling and intent classification) in task-oriented dialogue systems. As prior work has been mostly experimented on English datasets, we focus on five different languages, and consider a setting where limited data are available. We investigate the effectiveness of non-gradient based augmentation methods, involving simple text span substitutions and syntactic manipulations. Our experiments show that (i) augmentation is effective in all cases, particularly for slot filling; and (ii) it is beneficial for a joint intent-slot model based on multilingual BERT, both for limited data settings and when full training data is used.
