@inproceedings{hirasawa-etal-2019-multimodal,
title = "Multimodal Machine Translation with Embedding Prediction",
author = "Hirasawa, Tosho and
Yamagishi, Hayahide and
Matsumura, Yukio and
Komachi, Mamoru",
editor = "Kar, Sudipta and
Nadeem, Farah and
Burdick, Laura and
Durrett, Greg and
Han, Na-Rae",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-3012",
doi = "10.18653/v1/N19-3012",
pages = "86--91",
abstract = "Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for translating rare words. In NMT, pretrained word embeddings have been shown to improve NMT of low-resource domains, and a search-based approach is proposed to address the rare word problem. In this study, we effectively combine these two approaches in the context of multimodal NMT and explore how we can take full advantage of pretrained word embeddings to better translate rare words. We report overall performance improvements of 1.24 METEOR and 2.49 BLEU and achieve an improvement of 7.67 F-score for rare word translation.",
}
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<abstract>Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for translating rare words. In NMT, pretrained word embeddings have been shown to improve NMT of low-resource domains, and a search-based approach is proposed to address the rare word problem. In this study, we effectively combine these two approaches in the context of multimodal NMT and explore how we can take full advantage of pretrained word embeddings to better translate rare words. We report overall performance improvements of 1.24 METEOR and 2.49 BLEU and achieve an improvement of 7.67 F-score for rare word translation.</abstract>
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%0 Conference Proceedings
%T Multimodal Machine Translation with Embedding Prediction
%A Hirasawa, Tosho
%A Yamagishi, Hayahide
%A Matsumura, Yukio
%A Komachi, Mamoru
%Y Kar, Sudipta
%Y Nadeem, Farah
%Y Burdick, Laura
%Y Durrett, Greg
%Y Han, Na-Rae
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F hirasawa-etal-2019-multimodal
%X Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for translating rare words. In NMT, pretrained word embeddings have been shown to improve NMT of low-resource domains, and a search-based approach is proposed to address the rare word problem. In this study, we effectively combine these two approaches in the context of multimodal NMT and explore how we can take full advantage of pretrained word embeddings to better translate rare words. We report overall performance improvements of 1.24 METEOR and 2.49 BLEU and achieve an improvement of 7.67 F-score for rare word translation.
%R 10.18653/v1/N19-3012
%U https://aclanthology.org/N19-3012
%U https://doi.org/10.18653/v1/N19-3012
%P 86-91
Markdown (Informal)
[Multimodal Machine Translation with Embedding Prediction](https://aclanthology.org/N19-3012) (Hirasawa et al., NAACL 2019)
ACL
- Tosho Hirasawa, Hayahide Yamagishi, Yukio Matsumura, and Mamoru Komachi. 2019. Multimodal Machine Translation with Embedding Prediction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 86–91, Minneapolis, Minnesota. Association for Computational Linguistics.