Pivot Language for Low-Resource Machine Translation
This work addresses translation challenges for low-resource language pairs like Nepali-English, though it is incremental as it applies existing pivot methods to a specific case.
The paper tackles the problem of low-resource machine translation for Nepali-English by using Hindi as a pivot language, achieving a SacreBLEU score of 14.2 with a fully supervised method, which improves the baseline by 6.6 points.
Certain pairs of languages suffer from lack of a parallel corpus which is large in size and diverse in domain. One of the ways this is overcome is via use of a pivot language. In this paper we use Hindi as a pivot language to translate Nepali into English. We describe what makes Hindi a good candidate for the pivot. We discuss ways in which a pivot language can be used, and use two such approaches - the Transfer Method (fully supervised) and Backtranslation (semi-supervised) - to translate Nepali into English. Using the former, we are able to achieve a devtest Set SacreBLEU score of 14.2, which improves the baseline fully supervised score reported by (Guzman et al., 2019) by 6.6 points. While we are slightly below the semi-supervised baseline score of 15.1, we discuss what may have caused this under-performance, and suggest scope for future work.