Improving Zero-shot Cross-lingual Transfer between Closely Related Languages by Injecting Character-level Noise

Noëmi Aepli, Rico Sennrich

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Cross-lingual transfer between a high-resource language and its dialects or closely related language varieties should be facilitated by their similarity. However, current approaches that operate in the embedding space do not take surface similarity into account. This work presents a simple yet effective strategy to imrove cross-lingual transfer between closely related varieties. We propose to augment the data of the high-resource source language with character-level noise to make the model more robust towards spelling variations. Our strategy shows consistent improvements over several languages and tasks: Zero-shot transfer of POS tagging and topic identification between language varieties from the Finnic, West and North Germanic, and Western Romance language branches. Our work provides evidence for the usefulness of simple surface-level noise in improving transfer between language varieties.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: ACL 2022
EditorsSmaranda Muresan, Preslav Nakov, Aline Villavicencio
PublisherAssociation for Computational Linguistics
Number of pages10
ISBN (Print)978-1-955917-25-4
Publication statusPublished - 16 May 2022
Event60th Annual Meeting of the Association for Computational Linguistics - The Convention Centre Dublin, Dublin, Ireland
Duration: 22 May 202227 May 2022


Conference60th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2022
Internet address

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