@inproceedings{yu-etal-2021-language,
title = "Language Embeddings for Typology and Cross-lingual Transfer Learning",
author = "Yu, Dian and
He, Taiqi and
Sagae, Kenji",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.560",
doi = "10.18653/v1/2021.acl-long.560",
pages = "7210--7225",
abstract = "Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks without the use of parallel data. We generate dense embeddings for 29 languages using a denoising autoencoder, and evaluate the embeddings using the World Atlas of Language Structures (WALS) and two extrinsic tasks in a zero-shot setting: cross-lingual dependency parsing and cross-lingual natural language inference.",
}
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%0 Conference Proceedings
%T Language Embeddings for Typology and Cross-lingual Transfer Learning
%A Yu, Dian
%A He, Taiqi
%A Sagae, Kenji
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F yu-etal-2021-language
%X Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks without the use of parallel data. We generate dense embeddings for 29 languages using a denoising autoencoder, and evaluate the embeddings using the World Atlas of Language Structures (WALS) and two extrinsic tasks in a zero-shot setting: cross-lingual dependency parsing and cross-lingual natural language inference.
%R 10.18653/v1/2021.acl-long.560
%U https://aclanthology.org/2021.acl-long.560
%U https://doi.org/10.18653/v1/2021.acl-long.560
%P 7210-7225
Markdown (Informal)
[Language Embeddings for Typology and Cross-lingual Transfer Learning](https://aclanthology.org/2021.acl-long.560) (Yu et al., ACL-IJCNLP 2021)
ACL
- Dian Yu, Taiqi He, and Kenji Sagae. 2021. Language Embeddings for Typology and Cross-lingual Transfer Learning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7210–7225, Online. Association for Computational Linguistics.