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        <title>Latent Variable Model for Multi-modal Translation</title>
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    <name type="personal">
        <namePart type="given">Iacer</namePart>
        <namePart type="family">Calixto</namePart>
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        <namePart type="given">Miguel</namePart>
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    <name type="personal">
        <namePart type="given">Wilker</namePart>
        <namePart type="family">Aziz</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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            <namePart type="given">Lluís</namePart>
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    <abstract>In this work, we propose to model the interaction between visual and textual features for multi-modal neural machine translation (MMT) through a latent variable model. This latent variable can be seen as a multi-modal stochastic embedding of an image and its description in a foreign language. It is used in a target-language decoder and also to predict image features. Importantly, our model formulation utilises visual and textual inputs during training but does not require that images be available at test time. We show that our latent variable MMT formulation improves considerably over strong baselines, including a multi-task learning approach (Elliott and Kadar, 2017) and a conditional variational auto-encoder approach (Toyama et al., 2016). Finally, we show improvements due to (i) predicting image features in addition to only conditioning on them, (ii) imposing a constraint on the KL term to promote models with non-negligible mutual information between inputs and latent variable, and (iii) by training on additional target-language image descriptions (i.e. synthetic data).</abstract>
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    <identifier type="doi">10.18653/v1/P19-1642</identifier>
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        <date>2019-07</date>
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%0 Conference Proceedings
%T Latent Variable Model for Multi-modal Translation
%A Calixto, Iacer
%A Rios, Miguel
%A Aziz, Wilker
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F calixto-etal-2019-latent
%X In this work, we propose to model the interaction between visual and textual features for multi-modal neural machine translation (MMT) through a latent variable model. This latent variable can be seen as a multi-modal stochastic embedding of an image and its description in a foreign language. It is used in a target-language decoder and also to predict image features. Importantly, our model formulation utilises visual and textual inputs during training but does not require that images be available at test time. We show that our latent variable MMT formulation improves considerably over strong baselines, including a multi-task learning approach (Elliott and Kadar, 2017) and a conditional variational auto-encoder approach (Toyama et al., 2016). Finally, we show improvements due to (i) predicting image features in addition to only conditioning on them, (ii) imposing a constraint on the KL term to promote models with non-negligible mutual information between inputs and latent variable, and (iii) by training on additional target-language image descriptions (i.e. synthetic data).
%R 10.18653/v1/P19-1642
%U https://aclanthology.org/P19-1642
%U https://doi.org/10.18653/v1/P19-1642
%P 6392-6405

Markdown (Informal)

[Latent Variable Model for Multi-modal Translation](https://aclanthology.org/P19-1642) (Calixto et al., ACL 2019)

• Latent Variable Model for Multi-modal Translation (Calixto et al., ACL 2019)

ACL

• Iacer Calixto, Miguel Rios, and Wilker Aziz. 2019. Latent Variable Model for Multi-modal Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6392–6405, Florence, Italy. Association for Computational Linguistics.