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A Survey of Cross-lingual Word Embedding Models

The Journal of artificial intelligence research, 2019-01, Vol.65, p.569-631 [Peer Reviewed Journal]

2019. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.1.11640

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  • Title:
    A Survey of Cross-lingual Word Embedding Models
  • Author: Ruder, Sebastian ; Vulić, Ivan ; Søgaard, Anders
  • Subjects: Embedding ; Natural language processing ; Optimization ; Words (language)
  • Is Part Of: The Journal of artificial intelligence research, 2019-01, Vol.65, p.569-631
  • Description: 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.
  • Publisher: San Francisco: AI Access Foundation
  • Language: English
  • Identifier: ISSN: 1076-9757
    EISSN: 1943-5037
    DOI: 10.1613/jair.1.11640
  • Source: Open Access: DOAJ Directory of Open Access Journals
    American Association for Artificial Intelligence (Open Access)
    AUTh Library subscriptions: ProQuest Central
    Open Access: Freely Accessible Journals by multiple vendors
    Alma/SFX Local Collection
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