Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies

Maryam Aminian, Mohammad Sadegh Rasooli, Mona Diab


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
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8268–8274
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.663
DOI:
10.18653/v1/2020.emnlp-main.663
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.663.pdf