@inproceedings{mayer-martins-etal-2026-vocabulary,
title = "Vocabulary Shapes Cross-Lingual Variation of Word-Order Learnability in Language Models",
author = "Mayer Martins, Jonas and
Jumelet, Jaap and
Priesemann, Viola and
Beinborn, Lisa",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1510/",
doi = "10.18653/v1/2026.acl-long.1510",
pages = "32724--32740",
ISBN = "979-8-89176-390-6",
abstract = "Why do some languages like Czech permit free word order, while others like English do not? We address this question by pretraining transformer language models on a spectrum of synthetic word-order variants of natural languages. We observe that greater word-order irregularity consistently raises model surprisal, indicating reduced learnability. Sentence reversal, however, affects learnability only weakly. A coarse distinction of free- (e.g., Czech and Finnish) and fixed-word-order languages (e.g., English and French) does not explain cross-lingual variation. Instead, the structure of the word and subword vocabulary strongly predicts the model surprisal. Overall, vocabulary structure emerges as a key driver of computational word-order learnability across languages."
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%0 Conference Proceedings
%T Vocabulary Shapes Cross-Lingual Variation of Word-Order Learnability in Language Models
%A Mayer Martins, Jonas
%A Jumelet, Jaap
%A Priesemann, Viola
%A Beinborn, Lisa
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F mayer-martins-etal-2026-vocabulary
%X Why do some languages like Czech permit free word order, while others like English do not? We address this question by pretraining transformer language models on a spectrum of synthetic word-order variants of natural languages. We observe that greater word-order irregularity consistently raises model surprisal, indicating reduced learnability. Sentence reversal, however, affects learnability only weakly. A coarse distinction of free- (e.g., Czech and Finnish) and fixed-word-order languages (e.g., English and French) does not explain cross-lingual variation. Instead, the structure of the word and subword vocabulary strongly predicts the model surprisal. Overall, vocabulary structure emerges as a key driver of computational word-order learnability across languages.
%R 10.18653/v1/2026.acl-long.1510
%U https://aclanthology.org/2026.acl-long.1510/
%U https://doi.org/10.18653/v1/2026.acl-long.1510
%P 32724-32740
Markdown (Informal)
[Vocabulary Shapes Cross-Lingual Variation of Word-Order Learnability in Language Models](https://aclanthology.org/2026.acl-long.1510/) (Mayer Martins et al., ACL 2026)
ACL