Cross-lingual complex word identification with multitask learning

Joachim Bingel, Johannes Bjerva


Abstract
We approach the 2018 Shared Task on Complex Word Identification by leveraging a cross-lingual multitask learning approach. Our method is highly language agnostic, as evidenced by the ability of our system to generalize across languages, including languages for which we have no training data. In the shared task, this is the case for French, for which our system achieves the best performance. We further provide a qualitative and quantitative analysis of which words pose problems for our system.
Anthology ID:
W18-0518
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
166–174
Language:
URL:
https://www.aclweb.org/anthology/W18-0518
DOI:
10.18653/v1/W18-0518
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0518.pdf