@inproceedings{ruder-etal-2019-unsupervised,
title = "Unsupervised Cross-Lingual Representation Learning",
author = "Ruder, Sebastian and
S{\o}gaard, Anders and
Vuli{\'c}, Ivan",
editor = "Nakov, Preslav and
Palmer, Alexis",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-4007",
doi = "10.18653/v1/P19-4007",
pages = "31--38",
abstract = "In this tutorial, we provide a comprehensive survey of the exciting recent work on cutting-edge weakly-supervised and unsupervised cross-lingual word representations. After providing a brief history of supervised cross-lingual word representations, we focus on: 1) how to induce weakly-supervised and unsupervised cross-lingual word representations in truly resource-poor settings where bilingual supervision cannot be guaranteed; 2) critical examinations of different training conditions and requirements under which unsupervised algorithms can and cannot work effectively; 3) more robust methods for distant language pairs that can mitigate instability issues and low performance for distant language pairs; 4) how to comprehensively evaluate such representations; and 5) diverse applications that benefit from cross-lingual word representations (e.g., MT, dialogue, cross-lingual sequence labeling and structured prediction applications, cross-lingual IR).",
}
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%0 Conference Proceedings
%T Unsupervised Cross-Lingual Representation Learning
%A Ruder, Sebastian
%A Søgaard, Anders
%A Vulić, Ivan
%Y Nakov, Preslav
%Y Palmer, Alexis
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F ruder-etal-2019-unsupervised
%X In this tutorial, we provide a comprehensive survey of the exciting recent work on cutting-edge weakly-supervised and unsupervised cross-lingual word representations. After providing a brief history of supervised cross-lingual word representations, we focus on: 1) how to induce weakly-supervised and unsupervised cross-lingual word representations in truly resource-poor settings where bilingual supervision cannot be guaranteed; 2) critical examinations of different training conditions and requirements under which unsupervised algorithms can and cannot work effectively; 3) more robust methods for distant language pairs that can mitigate instability issues and low performance for distant language pairs; 4) how to comprehensively evaluate such representations; and 5) diverse applications that benefit from cross-lingual word representations (e.g., MT, dialogue, cross-lingual sequence labeling and structured prediction applications, cross-lingual IR).
%R 10.18653/v1/P19-4007
%U https://aclanthology.org/P19-4007
%U https://doi.org/10.18653/v1/P19-4007
%P 31-38
Markdown (Informal)
[Unsupervised Cross-Lingual Representation Learning](https://aclanthology.org/P19-4007) (Ruder et al., ACL 2019)
ACL
- Sebastian Ruder, Anders Søgaard, and Ivan Vulić. 2019. Unsupervised Cross-Lingual Representation Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, pages 31–38, Florence, Italy. Association for Computational Linguistics.