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        <title>Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data</title>
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    <name type="personal">
        <namePart type="given">Koel</namePart>
        <namePart type="family">Dutta Chowdhury</namePart>
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    <name type="personal">
        <namePart type="given">Mohammed</namePart>
        <namePart type="family">Hasanuzzaman</namePart>
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    <name type="personal">
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            <title>Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP</title>
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        <name type="personal">
            <namePart type="given">Reza</namePart>
            <namePart type="family">Haffari</namePart>
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        <name type="personal">
            <namePart type="given">Colin</namePart>
            <namePart type="family">Cherry</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Bahar</namePart>
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    <abstract>In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a low-resource language pair, Hindi and English, using synthetic data. A three-way parallel corpus which contains bilingual texts and corresponding images is required to train a MNMT system with image features. However, such a corpus is not available for low resource language pairs. To address this, we developed both a synthetic training dataset and a manually curated development/test dataset for Hindi based on an existing English-image parallel corpus. We used these datasets to build our image description translation system by adopting state-of-the-art MNMT models. Our results show that it is possible to train a MNMT system for low-resource language pairs through the use of synthetic data and that such a system can benefit from image features.</abstract>
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    <identifier type="doi">10.18653/v1/W18-3405</identifier>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data
%A Dutta Chowdhury, Koel
%A Hasanuzzaman, Mohammed
%A Liu, Qun
%Y Haffari, Reza
%Y Cherry, Colin
%Y Foster, George
%Y Khadivi, Shahram
%Y Salehi, Bahar
%S Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne
%F dutta-chowdhury-etal-2018-multimodal
%X In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a low-resource language pair, Hindi and English, using synthetic data. A three-way parallel corpus which contains bilingual texts and corresponding images is required to train a MNMT system with image features. However, such a corpus is not available for low resource language pairs. To address this, we developed both a synthetic training dataset and a manually curated development/test dataset for Hindi based on an existing English-image parallel corpus. We used these datasets to build our image description translation system by adopting state-of-the-art MNMT models. Our results show that it is possible to train a MNMT system for low-resource language pairs through the use of synthetic data and that such a system can benefit from image features.
%R 10.18653/v1/W18-3405
%U https://aclanthology.org/W18-3405
%U https://doi.org/10.18653/v1/W18-3405
%P 33-42

Markdown (Informal)

[Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data](https://aclanthology.org/W18-3405) (Dutta Chowdhury et al., ACL 2018)

• Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data (Dutta Chowdhury et al., ACL 2018)

ACL

• Koel Dutta Chowdhury, Mohammed Hasanuzzaman, and Qun Liu. 2018. Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data. In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, pages 33–42, Melbourne. Association for Computational Linguistics.