Analysis of Neural Machine Translation KANGRI Language by Unsupervised and Semi Supervised Methods

It is very challenging to work with low resource languages pairs as monolingual and parallel dataset do not exist or exist in a very small amount. Furthermore, there is a lack of digitization of the available written resources. This work provides a comparison and analysis of the neural machine translation system for low resource definitely endangered, Kangri (ISO 639-3xnr) language using unsupervised and semi supervised methods. For this a shared encoder with back translation machine translation system for both unsupervised and semi-supervised learning techniques and a language model with denoising autoencoder that uses fully unsupervised learning technique has been used. Kangri which is an Indo-Aryan language has Devanagari () script same as Hindi. The translation task is further complicated by the fact that Kangri is a morphologically rich language, and it does not have well defined linguistic rules. To remove out of vocabulary problem we have used different technique and in finally, we have provided the comparison of results by taking the different evaluation metrics which shows that semi supervised translation with semi supervised cross lingual word embedding has highest score as compared to other translation models.