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    <abstract>This paper introduces our neural machine translation system’s participation in the WAT 2022 shared translation task (team ID: sakura). We participated in the Parallel Data Filtering Task. Our approach based on Feature Decay Algorithms achieved +1.4 and +2.4 BLEU points for English to Japanese and Japanese to English respectively compared to the model trained on the full dataset, showing the effectiveness of FDA on in-domain data selection.</abstract>
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%0 Conference Proceedings
%T Rakuten’s Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity
%A Poncelas, Alberto
%A Effendi, Johanes
%A Htun, Ohnmar
%A Yadav, Sunil
%A Wang, Dongzhe
%A Jain, Saurabh
%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 poncelas-etal-2022-rakutens
%X This paper introduces our neural machine translation system’s participation in the WAT 2022 shared translation task (team ID: sakura). We participated in the Parallel Data Filtering Task. Our approach based on Feature Decay Algorithms achieved +1.4 and +2.4 BLEU points for English to Japanese and Japanese to English respectively compared to the model trained on the full dataset, showing the effectiveness of FDA on in-domain data selection.
%U https://aclanthology.org/2022.wat-1.7
%P 68-72

Markdown (Informal)

[Rakuten’s Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity](https://aclanthology.org/2022.wat-1.7) (Poncelas et al., WAT 2022)

• Rakuten’s Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity (Poncelas et al., WAT 2022)

ACL

• Alberto Poncelas, Johanes Effendi, Ohnmar Htun, Sunil Yadav, Dongzhe Wang, and Saurabh Jain. 2022. Rakuten’s Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity. In Proceedings of the 9th Workshop on Asian Translation, pages 68–72, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.