Sunil Yadav


2022

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Rakuten’s Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity
Alberto Poncelas | Johanes Effendi | Ohnmar Htun | Sunil Yadav | Dongzhe Wang | Saurabh Jain
Proceedings of the 9th Workshop on Asian Translation

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.

2021

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Rakuten’s Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation
Raymond Hendy Susanto | Dongzhe Wang | Sunil Yadav | Mausam Jain | Ohnmar Htun
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper introduces our neural machine translation systems’ participation in the WAT 2021 shared translation tasks (team ID: sakura). We participated in the (i) NICT-SAP, (ii) Japanese-English multimodal translation, (iii) Multilingual Indic, and (iv) Myanmar-English translation tasks. Multilingual approaches such as mBART (Liu et al., 2020) are capable of pre-training a complete, multilingual sequence-to-sequence model through denoising objectives, making it a great starting point for building multilingual translation systems. Our main focus in this work is to investigate the effectiveness of multilingual finetuning on such a multilingual language model on various translation tasks, including low-resource, multimodal, and mixed-domain translation. We further explore a multimodal approach based on universal visual representation (Zhang et al., 2019) and compare its performance against a unimodal approach based on mBART alone.