Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering

Ofir Arviv, Dmitry Nikolaev, Taelin Karidi, Omri Abend

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Despite the impressive growth of the abilities of multilingual language models, such as XLM-R and mT5, it has been shown that they still face difficulties when tackling typologically-distant languages, particularly in the low-resource setting. One obstacle for effective cross-lingual transfer is variability in word-order patterns. It can be potentially mitigated via source- or target-side word reordering, and numerous approaches to reordering have been proposed. However, they rely on language-specific rules, work on the level of POS tags, or only target the main clause, leaving subordinate clauses intact. To address these limitations, we present a new powerful reordering method, defined in terms of Universal Dependencies, that is able to learn fine-grained word-order patterns conditioned on the syntactic context from a small amount of annotated data and can be applied at all levels of the syntactic tree. We conduct experiments on a diverse set of tasks and show that our method consistently outperforms strong baselines over different language pairs and model architectures. This performance advantage holds true in both zero-shot and few-shot scenarios.

Original languageAmerican English
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationEMNLP 2023
PublisherAssociation for Computational Linguistics (ACL)
Number of pages19
ISBN (Electronic)9798891760615
StatePublished - 2023
Event2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Singapore, Singapore
Duration: 6 Dec 202310 Dec 2023

Publication series

NameFindings of the Association for Computational Linguistics: EMNLP 2023


Conference2023 Findings of the Association for Computational Linguistics: EMNLP 2023

Bibliographical note

Publisher Copyright:
© 2023 Association for Computational Linguistics.


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