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.
Cite
CITATION STYLE
Bingel, J., & Bjerva, J. (2018). Cross-lingual complex word identification with multitask learning. In Proceedings of the 13th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018 (pp. 166–174). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w18-0518
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