URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors

Patrick Littell, David R. Mortensen, Ke Lin, Katherine Kairis, Carlisle Turner, Lori Levin


Abstract
We introduce the URIEL knowledge base for massively multilingual NLP and the lang2vec utility, which provides information-rich vector identifications of languages drawn from typological, geographical, and phylogenetic databases and normalized to have straightforward and consistent formats, naming, and semantics. The goal of URIEL and lang2vec is to enable multilingual NLP, especially on less-resourced languages and make possible types of experiments (especially but not exclusively related to NLP tasks) that are otherwise difficult or impossible due to the sparsity and incommensurability of the data sources. lang2vec vectors have been shown to reduce perplexity in multilingual language modeling, when compared to one-hot language identification vectors.
Anthology ID:
E17-2002
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8–14
Language:
URL:
https://aclanthology.org/E17-2002
DOI:
Bibkey:
Cite (ACL):
Patrick Littell, David R. Mortensen, Ke Lin, Katherine Kairis, Carlisle Turner, and Lori Levin. 2017. URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 8–14, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors (Littell et al., EACL 2017)
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PDF:
https://aclanthology.org/E17-2002.pdf