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        <title>Experiences of Adapting Multimodal Machine Translation Techniques for Hindi</title>
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        <namePart type="given">Baban</namePart>
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        <namePart type="given">Dibyanayan</namePart>
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            <title>Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021)</title>
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            <namePart type="given">Thoudam</namePart>
            <namePart type="family">Doren Singh</namePart>
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    <abstract>Multimodal Neural Machine Translation (MNMT) is an interesting task in natural language processing (NLP) where we use visual modalities along with a source sentence to aid the source to target translation process. Recently, there has been a lot of works in MNMT frameworks to boost the performance of standalone Machine Translation tasks. Most of the prior works in MNMT tried to perform translation between two widely known languages (e.g. English-to-German, English-to-French ). In this paper, We explore the effectiveness of different state-of-the-art MNMT methods, which use various data oriented techniques including multimodal pre-training, for low resource languages. Although the existing methods works well on high resource languages, usability of those methods on low-resource languages is unknown. In this paper, we evaluate the existing methods on Hindi and report our findings.</abstract>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T Experiences of Adapting Multimodal Machine Translation Techniques for Hindi
%A Gain, Baban
%A Bandyopadhyay, Dibyanayan
%A Ekbal, Asif
%Y Doren Singh, Thoudam
%Y España i Bonet, Cristina
%Y Bandyopadhyay, Sivaji
%Y van Genabith, Josef
%S Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Online (Virtual Mode)
%F gain-etal-2021-experiences
%X Multimodal Neural Machine Translation (MNMT) is an interesting task in natural language processing (NLP) where we use visual modalities along with a source sentence to aid the source to target translation process. Recently, there has been a lot of works in MNMT frameworks to boost the performance of standalone Machine Translation tasks. Most of the prior works in MNMT tried to perform translation between two widely known languages (e.g. English-to-German, English-to-French ). In this paper, We explore the effectiveness of different state-of-the-art MNMT methods, which use various data oriented techniques including multimodal pre-training, for low resource languages. Although the existing methods works well on high resource languages, usability of those methods on low-resource languages is unknown. In this paper, we evaluate the existing methods on Hindi and report our findings.
%U https://aclanthology.org/2021.mmtlrl-1.7/
%P 40-44

Markdown (Informal)

[Experiences of Adapting Multimodal Machine Translation Techniques for Hindi](https://aclanthology.org/2021.mmtlrl-1.7/) (Gain et al., MMTLRL 2021)

• Experiences of Adapting Multimodal Machine Translation Techniques for Hindi (Gain et al., MMTLRL 2021)

ACL

• Baban Gain, Dibyanayan Bandyopadhyay, and Asif Ekbal. 2021. Experiences of Adapting Multimodal Machine Translation Techniques for Hindi. In Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021), pages 40–44, Online (Virtual Mode). INCOMA Ltd..