A Tulu Resource for Machine Translation
Manu Narayanan Noëmi Aepli
We present the first parallel dataset for English-Tulu translation. Tulu,
classified within the South Dravidian linguistic family branch, is
predominantly spoken by approximately 2.5 million individuals in southwestern
India. Our dataset is constructed by integrating human translations into the
multilingual machine translation resource FLORES-200. Furthermore, we use this
dataset for evaluation purposes in developing our English-Tulu machine
translation model. For the model's training, we leverage resources available
for related South Dravidian languages. We adopt a transfer learning approach
that exploits similarities between high-resource and low-resource languages.
This method enables the training of a machine translation system even in the
absence of parallel data between the source and target language, thereby
overcoming a significant obstacle in machine translation development for
low-resource languages. Our English-Tulu system, trained without using parallel
English-Tulu data, outperforms Google Translate by 19 BLEU points (in September
2023). The dataset and code are available here:
https://github.com/manunarayanan/Tulu-NMT.