@inproceedings{el-naggar-etal-2026-typological,
title = "Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languages",
author = "El-Naggar, Nadine and
Kuribayashi, Tatsuki and
Briscoe, Ted",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1632/",
pages = "37110--37123",
ISBN = "979-8-89176-459-0",
abstract = "Some properties of languages, e.g., subject-object-verb (SOV) word order, are more prevalent than others among the thousands of attested natural languages (NLs). Such typological commonality is often attributed to learning biases. Computational simulations, recently with language models (LMs), have facilitated the exploration of this theory. In this paper, we extend existing analyses of the relationship between LMs' learning biases and typological commonality on both data and model sides, focusing on: (i) cross-serial dependencies, the upper limit of attested syntactic complexity, and (ii) stack-based LMs (SLMs), potentially facilitating learning of hierarchical patterns. We first evaluate generalization of SLMs on cross-serial dependencies across diverse artificial languages and confirm that they struggle with such constructions. However, SLMs with limited working memory generalize better suggesting a possible basis for such inductive bias and thus the typological commonality of some word order configurations."
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%0 Conference Proceedings
%T Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languages
%A El-Naggar, Nadine
%A Kuribayashi, Tatsuki
%A Briscoe, Ted
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F el-naggar-etal-2026-typological
%X Some properties of languages, e.g., subject-object-verb (SOV) word order, are more prevalent than others among the thousands of attested natural languages (NLs). Such typological commonality is often attributed to learning biases. Computational simulations, recently with language models (LMs), have facilitated the exploration of this theory. In this paper, we extend existing analyses of the relationship between LMs’ learning biases and typological commonality on both data and model sides, focusing on: (i) cross-serial dependencies, the upper limit of attested syntactic complexity, and (ii) stack-based LMs (SLMs), potentially facilitating learning of hierarchical patterns. We first evaluate generalization of SLMs on cross-serial dependencies across diverse artificial languages and confirm that they struggle with such constructions. However, SLMs with limited working memory generalize better suggesting a possible basis for such inductive bias and thus the typological commonality of some word order configurations.
%U https://aclanthology.org/2026.emnlp-main.1632/
%P 37110-37123
Markdown (Informal)
[Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languages](https://aclanthology.org/2026.emnlp-main.1632/) (El-Naggar et al., EMNLP 2026)
ACL