@inproceedings{gella-etal-2017-image,
title = "Image Pivoting for Learning Multilingual Multimodal Representations",
author = "Gella, Spandana and
Sennrich, Rico and
Keller, Frank and
Lapata, Mirella",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1303",
doi = "10.18653/v1/D17-1303",
pages = "2839--2845",
abstract = "In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.",
}
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<abstract>In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Image Pivoting for Learning Multilingual Multimodal Representations
%A Gella, Spandana
%A Sennrich, Rico
%A Keller, Frank
%A Lapata, Mirella
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F gella-etal-2017-image
%X In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.
%R 10.18653/v1/D17-1303
%U https://aclanthology.org/D17-1303
%U https://doi.org/10.18653/v1/D17-1303
%P 2839-2845
Markdown (Informal)
[Image Pivoting for Learning Multilingual Multimodal Representations](https://aclanthology.org/D17-1303) (Gella et al., EMNLP 2017)
ACL