Abstract
Latest efforts on cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource, while they may largely suffer from biased transfer (e.g., either target-biased or source-biased) due to the inevitable linguistic disparity between languages. In this work, we investigate an unbiased UD- based XRE transfer by constructing a type of code-mixed UD forest. We first translate the sentence of the source language to the parallel target-side language, for both of which we parse the UD tree respectively. Then, we merge the source-/target-side UD structures as a unified code-mixed UD forest. With such forest features, the gaps of UD-based XRE between the training and predicting phases can be effectively closed. We conduct experiments on the ACE XRE benchmark datasets, where the results demonstrate that the proposed code-mixed UD forests help unbiased UD-based XRE transfer, with which we achieve significant XRE performance gains.- Anthology ID:
- 2023.findings-acl.599
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9395–9408
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.599
- DOI:
- 10.18653/v1/2023.findings-acl.599
- Cite (ACL):
- Hao Fei, Meishan Zhang, Min Zhang, and Tat-Seng Chua. 2023. Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 9395–9408, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction (Fei et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.findings-acl.599.pdf