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        <title>Improved English to Hindi Multimodal Neural Machine Translation</title>
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        <namePart type="given">Sahinur</namePart>
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        <namePart type="given">Ur</namePart>
        <namePart type="given">Rahman</namePart>
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            <title>Proceedings of the 8th Workshop on Asian Translation (WAT2021)</title>
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    <abstract>Machine translation performs automatic translation from one natural language to another. Neural machine translation attains a state-of-the-art approach in machine translation, but it requires adequate training data, which is a severe problem for low-resource language pairs translation. The concept of multimodal is introduced in neural machine translation (NMT) by merging textual features with visual features to improve low-resource pair translation. WAT2021 (Workshop on Asian Translation 2021) organizes a shared task of multimodal translation for English to Hindi. We have participated the same with team name CNLP-NITS-PP in two submissions: multimodal and text-only NMT. This work investigates phrase pairs injection via data augmentation approach and attains improvement over our previous work at WAT2020 on the same task in both text-only and multimodal NMT. We have achieved second rank on the challenge test set for English to Hindi multimodal translation where Bilingual Evaluation Understudy (BLEU) score of 39.28, Rank-based Intuitive Bilingual Evaluation Score (RIBES) 0.792097, and Adequacy-Fluency Metrics (AMFM) score 0.830230 respectively.</abstract>
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    <identifier type="doi">10.18653/v1/2021.wat-1.17</identifier>
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        <date>2021-aug</date>
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%0 Conference Proceedings
%T Improved English to Hindi Multimodal Neural Machine Translation
%A Laskar, Sahinur Rahman
%A Khilji, Abdullah Faiz Ur Rahman
%A Kaushik, Darsh
%A Pakray, Partha
%A Bandyopadhyay, Sivaji
%S Proceedings of the 8th Workshop on Asian Translation (WAT2021)
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F laskar-etal-2021-improved
%X Machine translation performs automatic translation from one natural language to another. Neural machine translation attains a state-of-the-art approach in machine translation, but it requires adequate training data, which is a severe problem for low-resource language pairs translation. The concept of multimodal is introduced in neural machine translation (NMT) by merging textual features with visual features to improve low-resource pair translation. WAT2021 (Workshop on Asian Translation 2021) organizes a shared task of multimodal translation for English to Hindi. We have participated the same with team name CNLP-NITS-PP in two submissions: multimodal and text-only NMT. This work investigates phrase pairs injection via data augmentation approach and attains improvement over our previous work at WAT2020 on the same task in both text-only and multimodal NMT. We have achieved second rank on the challenge test set for English to Hindi multimodal translation where Bilingual Evaluation Understudy (BLEU) score of 39.28, Rank-based Intuitive Bilingual Evaluation Score (RIBES) 0.792097, and Adequacy-Fluency Metrics (AMFM) score 0.830230 respectively.
%R 10.18653/v1/2021.wat-1.17
%U https://aclanthology.org/2021.wat-1.17
%U https://doi.org/10.18653/v1/2021.wat-1.17
%P 155-160

Markdown (Informal)

[Improved English to Hindi Multimodal Neural Machine Translation](https://aclanthology.org/2021.wat-1.17) (Laskar et al., WAT 2021)

• Improved English to Hindi Multimodal Neural Machine Translation (Laskar et al., WAT 2021)

ACL

• Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Darsh Kaushik, Partha Pakray, and Sivaji Bandyopadhyay. 2021. Improved English to Hindi Multimodal Neural Machine Translation. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 155–160, Online. Association for Computational Linguistics.