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
- Editors:
- Joel Tetreault, Jill Burstein, Ekaterina Kochmar, Claudia Leacock, Helen Yannakoudakis
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 166–174
- Language:
- URL:
- https://aclanthology.org/W18-0518
- DOI:
- 10.18653/v1/W18-0518
- Cite (ACL):
- Joachim Bingel and Johannes Bjerva. 2018. Cross-lingual complex word identification with multitask learning. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 166–174, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Cross-lingual complex word identification with multitask learning (Bingel & Bjerva, BEA 2018)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/W18-0518.pdf