Multitask and Multilingual Modelling for Lexical Analysis
Research output: Contribution to journal › Journal article › Research › peer-review
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 language | English |
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Journal | KI - Künstliche Intelligenz |
Volume | 32 |
Issue number | 4 |
Pages (from-to) | 287-290 |
ISSN | 0933-1875 |
DOIs | |
Publication status | Published - 2018 |
- Natural language processing, Deep learning, Multitask learning, Multilingual learning
Research areas
Links
- https://arxiv.org/pdf/1809.02428.pdf
Submitted manuscript
ID: 209170933