Rakuten’s Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity
2022-10-01WAT 2022Unverified0· sign in to hype
Alberto Poncelas, Johanes Effendi, Ohnmar Htun, Sunil Yadav, Dongzhe Wang, Saurabh Jain
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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.