@inproceedings{singla-etal-2018-multi,
title = "A Multi-task Approach to Learning Multilingual Representations",
author = "Singla, Karan and
Can, Dogan and
Narayanan, Shrikanth",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2035",
doi = "10.18653/v1/P18-2035",
pages = "214--220",
abstract = "We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone. Our model shows competitive performance in a standard cross-lingual document classification task. We also show the effectiveness of our method in a limited resource scenario.",
}
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%0 Conference Proceedings
%T A Multi-task Approach to Learning Multilingual Representations
%A Singla, Karan
%A Can, Dogan
%A Narayanan, Shrikanth
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F singla-etal-2018-multi
%X We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone. Our model shows competitive performance in a standard cross-lingual document classification task. We also show the effectiveness of our method in a limited resource scenario.
%R 10.18653/v1/P18-2035
%U https://aclanthology.org/P18-2035
%U https://doi.org/10.18653/v1/P18-2035
%P 214-220
Markdown (Informal)
[A Multi-task Approach to Learning Multilingual Representations](https://aclanthology.org/P18-2035) (Singla et al., ACL 2018)
ACL
- Karan Singla, Dogan Can, and Shrikanth Narayanan. 2018. A Multi-task Approach to Learning Multilingual Representations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 214–220, Melbourne, Australia. Association for Computational Linguistics.