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2020 AACL AACL 2020

HW-TSC’s Participation in the WAT 2020 Indic Languages Multilingual Task

Abstract

AbstractThis paper describes our work in the WAT 2020 Indic Multilingual Translation Task. We participated in all 7 language pairs (En<->Bn/Hi/Gu/Ml/Mr/Ta/Te) in both directions under the constrained condition—using only the officially provided data. Using transformer as a baseline, our Multi->En and En->Multi translation systems achieve the best performances. Detailed data filtering and data domain selection are the keys to performance enhancement in our experiment, with an average improvement of 2.6 BLEU scores for each language pair in the En->Multi system and an average improvement of 4.6 BLEU scores regarding the Multi->En. In addition, we employed language independent adapter to further improve the system performances. Our submission obtains competitive results in the final evaluation.

🚀 Conference Pioneer — AACL 2020
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Natural Language Processing, Reinforcement Learning, Speech & Audio
🌉 Interdisciplinary Bridge — Deep Learning and Natural Language Processing
🧭 Keyword Pioneer — language adapter
🐣 Hot Topic Early Bird — indic language

Authors

Zhengzhe Yu , Zhanglin Wu , Xiaoyu Chen , Daimeng Wei , Hengchao Shang , Jiaxin Guo , Zongyao Li , Minghan Wang , Liangyou Li , Lizhi Lei , Hao Yang , Ying Qin

Topics

Natural Language Processing > Applications > Machine Translation Natural Language Processing > Generation > Machine Translation Deep Learning > Models > Transformers

Keywords

multilingual translation neural machine translation data filtering indic language language adapter transformer model
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