Abstract
We consider unsupervised cross-lingual transfer on two tasks, viz., sentence-level argumentation mining and standard POS tagging. We combine direct transfer using bilingual embeddings with annotation projection, which projects labels across unlabeled parallel data. We do so by either merging respective source and target language datasets or alternatively by using multi-task learning. Our combination strategy considerably improves upon both direct transfer and projection with few available parallel sentences, the most realistic scenario for many low-resource target languages.- Anthology ID:
- W18-5216
- Volume:
- Proceedings of the 5th Workshop on Argument Mining
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Noam Slonim, Ranit Aharonov
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 131–143
- Language:
- URL:
- https://aclanthology.org/W18-5216
- DOI:
- 10.18653/v1/W18-5216
- Cite (ACL):
- Steffen Eger, Andreas Rücklé, and Iryna Gurevych. 2018. PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection. In Proceedings of the 5th Workshop on Argument Mining, pages 131–143, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection (Eger et al., ArgMining 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W18-5216.pdf
- Code
- UKPLab/emnlp2018-argmin-workshop-pd3