Abstract
Zero-resource cross-lingual transfer approaches aim to apply supervised modelsfrom a source language to unlabelled target languages. In this paper we performan in-depth study of the two main techniques employed so far for cross-lingualzero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translation and annotation projection(data-based cross-lingual transfer) as an effective technique for cross-lingualsequence labelling, in this paper we experimentally demonstrate that highcapacity multilingual language models applied in a zero-shot (model-basedcross-lingual transfer) setting consistently outperform data-basedcross-lingual transfer approaches. A detailed analysis of our results suggeststhat this might be due to important differences in language use. Morespecifically, machine translation often generates a textual signal which isdifferent to what the models are exposed to when using gold standard data,which affects both the fine-tuning and evaluation processes. Our results alsoindicate that data-based cross-lingual transfer approaches remain a competitiveoption when high-capacity multilingual language models are not available.- Anthology ID:
- 2022.findings-emnlp.478
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6403–6416
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.478
- DOI:
- 10.18653/v1/2022.findings-emnlp.478
- Cite (ACL):
- Iker García-Ferrero, Rodrigo Agerri, and German Rigau. 2022. Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6403–6416, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings (García-Ferrero et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.findings-emnlp.478.pdf