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        <title>Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System</title>
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    <name type="personal">
        <namePart type="given">Hardik</namePart>
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        <namePart type="given">Mauajama</namePart>
        <namePart type="family">Firdaus</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Pushpak</namePart>
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            <namePart type="given">Anna</namePart>
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    <abstract>Multimodal dialogue systems have opened new frontiers in the traditional goal-oriented dialogue systems. The state-of-the-art dialogue systems are primarily based on unimodal sources, predominantly the text, and hence cannot capture the information present in the other sources such as videos, audios, images etc. With the availability of large scale multimodal dialogue dataset (MMD) (Saha et al., 2018) on the fashion domain, the visual appearance of the products is essential for understanding the intention of the user. Without capturing the information from both the text and image, the system will be incapable of generating correct and desirable responses. In this paper, we propose a novel position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance. Our evaluation shows that the proposed model can generate appropriate responses while preserving the position and attribute information. Experimental results also prove that our proposed approach attains superior performance compared to the baseline models, and outperforms the state-of-the-art approaches on text similarity based evaluation metrics.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1540</identifier>
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%0 Conference Proceedings
%T Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System
%A Chauhan, Hardik
%A Firdaus, Mauajama
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%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 chauhan-etal-2019-ordinal
%X Multimodal dialogue systems have opened new frontiers in the traditional goal-oriented dialogue systems. The state-of-the-art dialogue systems are primarily based on unimodal sources, predominantly the text, and hence cannot capture the information present in the other sources such as videos, audios, images etc. With the availability of large scale multimodal dialogue dataset (MMD) (Saha et al., 2018) on the fashion domain, the visual appearance of the products is essential for understanding the intention of the user. Without capturing the information from both the text and image, the system will be incapable of generating correct and desirable responses. In this paper, we propose a novel position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance. Our evaluation shows that the proposed model can generate appropriate responses while preserving the position and attribute information. Experimental results also prove that our proposed approach attains superior performance compared to the baseline models, and outperforms the state-of-the-art approaches on text similarity based evaluation metrics.
%R 10.18653/v1/P19-1540
%U https://aclanthology.org/P19-1540
%U https://doi.org/10.18653/v1/P19-1540
%P 5437-5447

Markdown (Informal)

[Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System](https://aclanthology.org/P19-1540) (Chauhan et al., ACL 2019)

• Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System (Chauhan et al., ACL 2019)

ACL

• Hardik Chauhan, Mauajama Firdaus, Asif Ekbal, and Pushpak Bhattacharyya. 2019. Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5437–5447, Florence, Italy. Association for Computational Linguistics.