This paper discusses the details of submission made by team Translation Techies to the Shared Task on Machine Translation in Dravidian languages- ACL 2022. In connection to the task, five language pairs were provided to test the accuracy of submitted model. A baseline transformer model with Neural Machine Translation(NMT) technique is used which has been taken directly from the OpenNMT framework. On this baseline model, tokenization is applied using the IndicNLP library. Finally, the evaluation is performed using the BLEU scoring mechanism.

Kannada is one of the major spoken classical languages in India. It is morphologically rich and highly agglutinative in nature. One of the important grammatical aspects is the concept of sandhi(euphonic change). There has not been a sandhi generator for Kannada and this work aims at basic sandhi generation. In this paper, we present algorithms for lopa and Adesha sandhi using a rule-based approach. The proposed method generates the sandhied word and corresponding sandhi without any help of dictionary. This work is significant for agglutinative languages especially to Dravidian languages and can be used to enhance the vocabulary for language related tasks.