IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task

Baban Gain, Dibyanayan Bandyopadhyay, Asif Ekbal

Abstract
Neural Machine Translation (NMT) is a predominant machine translation technology nowadays because of its end-to-end trainable flexibility. However, NMT still struggles to translate properly in low-resource settings specifically on distant language pairs. One way to overcome this is to use the information from other modalities if available. The idea is that despite differences in languages, both the source and target language speakers see the same thing and the visual representation of both the source and target is the same, which can positively assist the system. Multimodal information can help the NMT system to improve the translation by removing ambiguity on some phrases or words. We participate in the 8th Workshop on Asian Translation (WAT - 2021) for English-Hindi multimodal translation task and achieve 42.47 and 37.50 BLEU points for Evaluation and Challenge subset, respectively.
Anthology ID:
2021.wat-1.18
Volume:
Proceedings of the 8th Workshop on Asian Translation (WAT2021)
Month:
August
Year:
2021
Address:
Online
Editors:
Toshiaki Nakazawa, Hideki Nakayama, Isao Goto, Hideya Mino, Chenchen Ding, Raj Dabre, Anoop Kunchukuttan, Shohei Higashiyama, Hiroshi Manabe, Win Pa Pa, Shantipriya Parida, Ondřej Bojar, Chenhui Chu, Akiko Eriguchi, Kaori Abe, Yusuke Oda, Katsuhito Sudoh, Sadao Kurohashi, Pushpak Bhattacharyya
Venue:
WAT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
161–165
Language:
URL:
https://aclanthology.org/2021.wat-1.18
DOI:
10.18653/v1/2021.wat-1.18
Bibkey:
Cite (ACL):
Baban Gain, Dibyanayan Bandyopadhyay, and Asif Ekbal. 2021. IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 161–165, Online. Association for Computational Linguistics.
Cite (Informal):
IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task (Gain et al., WAT 2021)
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PDF:
https://preview.aclanthology.org/naacl24-info/2021.wat-1.18.pdf
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