  This paper presents a summary of the findings that we obtained based on the
shared task on machine translation of Dravidian languages. We stood first in
three of the five sub-tasks which were assigned to us for the main shared task.
We carried out neural machine translation for the following five language
pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Malayalam, Kannada to
Sanskrit, and Kannada to Tulu. The datasets for each of the five language pairs
were used to train various translation models, including Seq2Seq models such as
LSTM, bidirectional LSTM, Conv2Seq, and training state-of-the-art as
transformers from scratch, and fine-tuning already pre-trained models. For some
models involving monolingual corpora, we implemented backtranslation as well.
These models' accuracy was later tested with a part of the same dataset using
BLEU score as an evaluation metric.
