Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution
By Xavier García, Noah Constant, Ankur P. Parikh, Orhan Firat. 2021
53 citations recorded by the source corpus.
We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models, paving the way towards efficient continual learning for multilingual machine translation. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts, incurs only minor degradation on the translation performance for the original language pairs and provides competitive performance even in the case where we only possess monolingual data for the new languages.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Machine Translation, Meta & Continual Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Hang Su
- Jun Zhu
- Liyuan Wang
- Xingxing Zhang
- Tianyu Pang
- Yinpeng Dong
- Vamsi Krishna Ithapu
- Mirella Lapata
In Computer Vision and Pattern Recognition
- Hang Su
- Jun Zhu
- Liyuan Wang
- Xingxing Zhang
- Yinpeng Dong
- Vamsi Krishna Ithapu
- Alan L. Yuille
- Tianyu Pang
In Meta & Continual Learning
- Xingxing Zhang
- Liyuan Wang
- Hang Su
- Jun Zhu
- Simone Scardapane
- Valerio Marsocci
- Liyuan Wang
- Hongwei Yan
Citations in the corpus
- A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “The applications of continual learning in NLP are also highly diverse, such as dialogue system [130], [131], [260], [260], [282], [298], [437], text classification [87], [172], [325], [460], sentence generation [246], [298], [499], relation learning [343], [357], [455], [465], neural machine transla”