PICT@DravidianLangTech-ACL2022: Neural Machine Translation On Dravidian Languages
This work addresses machine translation for low-resource Dravidian languages, but it is incremental as it applies existing methods to new data.
The paper tackled neural machine translation for five Dravidian language pairs, achieving first place in three sub-tasks of a shared task, with performance evaluated using BLEU scores.
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.