In NMT, leveraging unlabeled monolingual data is relatively common since collecting monolingual data is much easier and of lower cost than parallel data.

Leveraging Mono-Lingual Data for Low-Resource NMT

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

Columbia University

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.


Low-resource neural machine translation

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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