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        <title>PICT@WAT 2022: Neural Machine Translation Systems for Indic Languages</title>
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    <abstract>Translation entails more than simply translating words from one language to another. It is vitally essential for effective cross-cultural communication, thus making good translation systems an important requirement. We describe our systems in this paper, which were submitted to the WAT 2022 translation shared tasks. As part of the Multi-modal translation tasks’ text-only translation sub-tasks, we submitted three Neural Machine Translation systems based on Transformer models for English to Malayalam, English to Bengali, and English to Hindi text translation. We found significant results on the leaderboard for English-Indic (en-xx) systems utilizing BLEU and RIBES scores as comparative metrics in our studies. For the respective translations of English to Malayalam, Bengali, and Hindi, we obtained BLEU scores of 19.50, 32.90, and 41.80 for the challenge subset and 30.60, 39.80, and 42.90 on the benchmark evaluation subset data.</abstract>
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%0 Conference Proceedings
%T PICT@WAT 2022: Neural Machine Translation Systems for Indic Languages
%A Patil, Anupam
%A Joshi, Isha
%A Kadam, Dipali
%S Proceedings of the 9th Workshop on Asian Translation
%D 2022
%8 October
%I International Conference on Computational Linguistics
%C Gyeongju, Republic of Korea
%F patil-etal-2022-pict
%X Translation entails more than simply translating words from one language to another. It is vitally essential for effective cross-cultural communication, thus making good translation systems an important requirement. We describe our systems in this paper, which were submitted to the WAT 2022 translation shared tasks. As part of the Multi-modal translation tasks’ text-only translation sub-tasks, we submitted three Neural Machine Translation systems based on Transformer models for English to Malayalam, English to Bengali, and English to Hindi text translation. We found significant results on the leaderboard for English-Indic (en-xx) systems utilizing BLEU and RIBES scores as comparative metrics in our studies. For the respective translations of English to Malayalam, Bengali, and Hindi, we obtained BLEU scores of 19.50, 32.90, and 41.80 for the challenge subset and 30.60, 39.80, and 42.90 on the benchmark evaluation subset data.
%U https://aclanthology.org/2022.wat-1.13
%P 106-110

Markdown (Informal)

[PICT@WAT 2022: Neural Machine Translation Systems for Indic Languages](https://aclanthology.org/2022.wat-1.13) (Patil et al., WAT 2022)

• PICT@WAT 2022: Neural Machine Translation Systems for Indic Languages (Patil et al., WAT 2022)

ACL

• Anupam Patil, Isha Joshi, and Dipali Kadam. 2022. PICT@WAT 2022: Neural Machine Translation Systems for Indic Languages. In Proceedings of the 9th Workshop on Asian Translation, pages 106–110, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.