A Survey of Cross-lingual Word Embedding Models
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A Survey of Cross-lingual Word Embedding Models. / Ruder, Sebastian; Vulić, Ivan; Søgaard, Anders.
I: The Journal of Artificial Intelligence Research, Bind 65, 2019, s. 569-631.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - A Survey of Cross-lingual Word Embedding Models
AU - Ruder, Sebastian
AU - Vulić, Ivan
AU - Søgaard, Anders
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
U2 - 10.1613/jair.1.11640
DO - 10.1613/jair.1.11640
M3 - Journal article
VL - 65
SP - 569
EP - 631
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
SN - 1076-9757
ER -
ID: 240408487