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Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP

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Reichart, Roi 
Korhonen, Anna 
Vulic, I 


The transfer or share of knowledge between languages is a popular solution to resource scarcity in NLP. However, the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures. Frameworks such as Universal Dependencies (UD) are designed to be cross-lingually consistent, but even in carefully designed resources trees representing equivalent sentences may not always overlap. In this paper, we measure cross-lingual syntactic variation, or anisomorphism, in the UD treebank collection, considering both morphological and structural properties. We show that reducing the level of anisomorphism yields consistent gains in cross-lingual transfer tasks. We introduce a source language selection procedure that facilitates effective cross-lingual parser transfer, and propose a typologically driven method for syntactic tree processing which reduces anisomorphism. Our results show the effectiveness of this method for both machine translation and cross-lingual sentence similarity, demonstrating the importance of syntactic structure compatibility for boosting cross-lingual transfer in NLP.



Journal Title

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)

Conference Name

56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)

Journal ISSN

Volume Title


Association for Computational Linguistics
European Research Council (648909)