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        <title>Investigation of English to Hindi Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation</title>
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        <namePart type="given">Sahinur</namePart>
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    <name type="personal">
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    <abstract>Machine translation translates one natural language to another, a well-defined natural language processing task. Neural machine translation (NMT) is a widely accepted machine translation approach, but it requires a sufficient amount of training data, which is a challenging issue for low-resource pair translation. Moreover, the multimodal concept utilizes text and visual features to improve low-resource pair translation. WAT2022 (Workshop on Asian Translation 2022) organizes (hosted by the COLING 2022) English to Hindi multimodal translation task where we have participated as a team named CNLP-NITS-PP in two tracks: 1) text-only and 2) multimodal translation. Herein, we have proposed a transliteration-based phrase pairs augmentation approach, which shows improvement in the multimodal translation task. We have attained the second best results on the challenge test set for English to Hindi multimodal translation with BLEU score of 39.30, and a RIBES score of 0.791468.</abstract>
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%0 Conference Proceedings
%T Investigation of English to Hindi Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation
%A Laskar, Sahinur Rahman
%A Singh, Rahul
%A Karim, Md Faizal
%A Manna, Riyanka
%A Pakray, Partha
%A Bandyopadhyay, Sivaji
%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 laskar-etal-2022-investigation-english
%X Machine translation translates one natural language to another, a well-defined natural language processing task. Neural machine translation (NMT) is a widely accepted machine translation approach, but it requires a sufficient amount of training data, which is a challenging issue for low-resource pair translation. Moreover, the multimodal concept utilizes text and visual features to improve low-resource pair translation. WAT2022 (Workshop on Asian Translation 2022) organizes (hosted by the COLING 2022) English to Hindi multimodal translation task where we have participated as a team named CNLP-NITS-PP in two tracks: 1) text-only and 2) multimodal translation. Herein, we have proposed a transliteration-based phrase pairs augmentation approach, which shows improvement in the multimodal translation task. We have attained the second best results on the challenge test set for English to Hindi multimodal translation with BLEU score of 39.30, and a RIBES score of 0.791468.
%U https://aclanthology.org/2022.wat-1.15
%P 117-122

Markdown (Informal)

[Investigation of English to Hindi Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation](https://aclanthology.org/2022.wat-1.15) (Laskar et al., WAT 2022)

• Investigation of English to Hindi Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation (Laskar et al., WAT 2022)

ACL

• Sahinur Rahman Laskar, Rahul Singh, Md Faizal Karim, Riyanka Manna, Partha Pakray, and Sivaji Bandyopadhyay. 2022. Investigation of English to Hindi Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation. In Proceedings of the 9th Workshop on Asian Translation, pages 117–122, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.