URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors
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.
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Language IdentificationLanguage ModelingLanguage ModellingMultilingual NLPSimilar Papers 제목 키워드 기반
URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base
URIEL is a knowledge base offering geographical, phylogenetic, and typological vector representations for 7970 languages. It includes distance measures between these vectors for 4005 languages, which are accessible via t…
A Reproducibility Study on Quantifying Language Similarity: The Impact of Missing Values in the URIEL Knowledge Base
In the pursuit of supporting more languages around the world, tools that characterize properties of languages play a key role in expanding the existing multilingual NLP research. In this study, we focus on a widely used …
Missing ValuesMultilingual NLPSimple Additions, Substantial Gains: Expanding Scripts, Languages, and Lineage Coverage in URIEL+
The URIEL+ linguistic knowledge base supports multilingual research by encoding languages through geographic, genetic, and typological vectors. However, data sparsity (e.g. missing feature types, incomplete language entr…
Cross-Lingual TransferLess is More: The Effectiveness of Compact Typological Language Representations
Linguistic feature datasets such as URIEL+ are valuable for modelling cross-lingual relationships, but their high dimensionality and sparsity, especially for low-resource languages, limit the effectiveness of distance me…
LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization
Pretrained language models (PLMs) have become remarkably adept at task and language generalization. Nonetheless, they often fail when faced with unseen languages. In this work, we present LinguAlchemy, a regularization m…
intent-classificationIntent ClassificationLanguage ModellingNews Classification+1