Multitask and Multilingual Modelling for Lexical Analysis
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Tidsskrift | KI - Künstliche Intelligenz |
Vol/bind | 32 |
Udgave nummer | 4 |
Sider (fra-til) | 287-290 |
ISSN | 0933-1875 |
DOI | |
Status | Udgivet - 2018 |
Links
- https://arxiv.org/pdf/1809.02428.pdf
Indsendt manuskript
ID: 209170933