Abstract
We describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available. We leverage a multitask learning framework coupled with annotation projection. We use syntactic parsing as the auxiliary task in our multitask setup. Our annotation projection experiments from English to Czech show that our multitask setup yields 3.1% (4.2%) improvement in labeled F1-score on in-domain (out-of-domain) test set compared to a single-task baseline.- Anthology ID:
- 2020.emnlp-main.663
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8268–8274
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.663
- DOI:
- 10.18653/v1/2020.emnlp-main.663
- Cite (ACL):
- Maryam Aminian, Mohammad Sadegh Rasooli, and Mona Diab. 2020. Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8268–8274, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies (Aminian et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.emnlp-main.663.pdf