<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mohammadshahi-etal-2019-aligning">
    <titleInfo>
        <title>Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Alireza</namePart>
        <namePart type="family">Mohammadshahi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rémi</namePart>
        <namePart type="family">Lebret</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Karl</namePart>
        <namePart type="family">Aberer</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Aditya</namePart>
            <namePart type="family">Mogadala</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dietrich</namePart>
            <namePart type="family">Klakow</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sandro</namePart>
            <namePart type="family">Pezzelle</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Hong Kong, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space while adapting the alignment of word embeddings between existing languages in our model. We show that our approach enables better generalization, achieving state-of-the-art performance in text-to-image and image-to-text retrieval task, and caption-caption similarity task. Two multimodal multilingual datasets are used for evaluation: Multi30k with German and English captions and Microsoft-COCO with English and Japanese captions.</abstract>
    <identifier type="citekey">mohammadshahi-etal-2019-aligning</identifier>
    <identifier type="doi">10.18653/v1/D19-6402</identifier>
    <location>
        <url>https://aclanthology.org/D19-6402</url>
    </location>
    <part>
        <date>2019-11</date>
        <extent unit="page">
            <start>11</start>
            <end>17</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task
%A Mohammadshahi, Alireza
%A Lebret, Rémi
%A Aberer, Karl
%Y Mogadala, Aditya
%Y Klakow, Dietrich
%Y Pezzelle, Sandro
%Y Moens, Marie-Francine
%S Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F mohammadshahi-etal-2019-aligning
%X In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space while adapting the alignment of word embeddings between existing languages in our model. We show that our approach enables better generalization, achieving state-of-the-art performance in text-to-image and image-to-text retrieval task, and caption-caption similarity task. Two multimodal multilingual datasets are used for evaluation: Multi30k with German and English captions and Microsoft-COCO with English and Japanese captions.
%R 10.18653/v1/D19-6402
%U https://aclanthology.org/D19-6402
%U https://doi.org/10.18653/v1/D19-6402
%P 11-17

Markdown (Informal)

[Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task](https://aclanthology.org/D19-6402) (Mohammadshahi et al., 2019)

• Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (Mohammadshahi et al., 2019)

ACL

• Alireza Mohammadshahi, Rémi Lebret, and Karl Aberer. 2019. Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task. In Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN), pages 11–17, Hong Kong, China. Association for Computational Linguistics.