Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings

Iker García-Ferrero, Rodrigo Agerri, German Rigau


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.478.pdf
Software:
 2022.findings-emnlp.478.software.zip