Work

A Survey of Cross-lingual Word Embedding Models

Year: 2019

Type: article

Abstract: Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent, modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.

Source: Journal of Artificial Intelligence Research

DOI: 10.1613/jair.1.11640

Authors: Sebastian Ruder, Ivan Vulić, Anders Søgaard

Institutions: Ollscoil na Gaillimhe – University of Galway, University of Cambridge

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: DeepMind, Science Foundation Ireland, Irish Research Council, H2020 European Research Council