From Bilingual to Multilingual Neural Machine Translation by Incremental Training
By Carlos Escolano, Marta Ruiz Costa-jussà, José A. R. Fonollosa. 2019
33 citations recorded by the source corpus.
Multilingual Neural Machine Translation approaches are based on the use of task specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to state-of-the-art in the WMT task.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Xavier García
- Noah Constant
- Orhan Firat
- Ankur P. Parikh
- Luke S. Zettlemoyer
- Graham Neubig
- Mohit Bansal
- Percy Liang
Citations in the corpus
- Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution (2021, background): “Other works (Zoph et al., 2016; Lakew et al., 2018, 2019; Escolano et al., 2019) have studied repurposing translation models as initializations for bilingual models for a target low-resource lan-”