Multitask and Multilingual Modelling for Lexical Analysis

Research output: Contribution to journalJournal articlepeer-review

  • Johannes Bjerva
In Natural Language Processing (NLP), one traditionally considers a single task (e.g.part-of-speech tagging) for a single language (e.g.English) at a time. However, recent work has shown that it can be beneficial to take advantage of relatedness between tasks, as well as between languages. In this work I examine the concept of relatedness and explore how it can be utilised to build NLP models that require less manually annotated data. A large selection of NLP tasks is investigated for a substantial language sample comprising 60 languages. The results show potential for joint multitask and multilingual modelling, and hints at linguistic insights which can be gained from such models.
Original languageEnglish
JournalKI - Künstliche Intelligenz
Volume32
Issue number4
Pages (from-to)287-290
ISSN0933-1875
DOIs
Publication statusPublished - 2018

    Research areas

  • Natural language processing, Deep learning, Multitask learning, Multilingual learning

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