The following data types have been leveraged to aid neural machine translation 
 for low-resource language pairs:
- monolingual data
- data from auxiliary languages 
- multi-modal data

Data Types Used to Help NMT for a Low-Resource Language Pair

Low-resource neural machine translation refers to NMT tasks in the setting of having a lack of parallel sentences to train neural machine translation models - i.e. there is not a very large corpus of parallel data between the source and target languages.


Columbia University

NMT is powered by deep learning and enjoys two advantages over traditional approaches such as rule-based machine translation and statistical machine translation - it does not require linguistic professional input, and it can take into account contextual information in the entire sentence, which leads to high quality and fluent translations. The limitation of NMT is that it requires a high scale of parallel data for model training. 

Neural Machine Translation (NMT)

Wang, R., Tan, X., Luo, R., Qin, T., & Liu, T.-Y. (8 2021). A Survey on Low-Resource Neural Machine Translation. Στο Z.-H. Zhou (Επιμ.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 (σσ. 4636–4643). doi:10.24963/ijcai.2021/629


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