@inproceedings{calixto-liu-2017-sentence,
title = "Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing",
author = "Calixto, Iacer and
Liu, Qun",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/978-954-452-049-6_020",
doi = "10.26615/978-954-452-049-6_020",
pages = "139--148",
abstract = "We propose a novel discriminative ranking model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality. To that end, we introduce an objective function that uses pairwise ranking adapted to the case of three or more input sources. We compare our model against different baselines, and evaluate the robustness of our embeddings on image{--}sentence ranking (ISR), semantic textual similarity (STS), and neural machine translation (NMT). We find that the additional multilingual signals lead to improvements on all three tasks, and we highlight that our model can be used to consistently improve the adequacy of translations generated with NMT models when re-ranking n-best lists.",
}
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%0 Conference Proceedings
%T Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing
%A Calixto, Iacer
%A Liu, Qun
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F calixto-liu-2017-sentence
%X We propose a novel discriminative ranking model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality. To that end, we introduce an objective function that uses pairwise ranking adapted to the case of three or more input sources. We compare our model against different baselines, and evaluate the robustness of our embeddings on image–sentence ranking (ISR), semantic textual similarity (STS), and neural machine translation (NMT). We find that the additional multilingual signals lead to improvements on all three tasks, and we highlight that our model can be used to consistently improve the adequacy of translations generated with NMT models when re-ranking n-best lists.
%R 10.26615/978-954-452-049-6_020
%U https://doi.org/10.26615/978-954-452-049-6_020
%P 139-148
Markdown (Informal)
[Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing](https://doi.org/10.26615/978-954-452-049-6_020) (Calixto & Liu, RANLP 2017)
ACL