---
title: "Function Words as Statistical Cues for Language Learning (2026)"
description: "The paper asks which statistical features of linear input could help a learner arrive at abstract grammar. 63 references, cited by 1 paper here."
authors: ["Xiulin Yang", "Heidi R. Getz", "Ethan Gotlieb Wilcox"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.728/"
doi: "10.18653/v1/2026.acl-long.728"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yang-2026-function-words-as-statistical-cues/"
---
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# Function Words as Statistical Cues for Language Learning

**Authors:** [Xiulin Yang](https://md.research.elamur.ai/free/en/authors/xiulin-yang/), Heidi R. Getz and [Ethan Gotlieb Wilcox](https://md.research.elamur.ai/free/en/authors/ethan-wilcox/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.728/) · [PDF](https://aclanthology.org/2026.acl-long.728.pdf) · [DOI 10.18653/v1/2026.acl-long.728](https://doi.org/10.18653/v1/2026.acl-long.728) · [OpenAlex](https://openalex.org/W7166808734)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/yang-2026-function-words-as-statistical-cues/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks which statistical features of linear input could help a learner arrive at abstract grammar. It focuses on function words. Acquisition research has credited them with three traits: very high frequency, tight links to particular syntactic structures, and placement at phrase edges. Most of that evidence came from English or small artificial languages.

The authors first test the three traits on UD v2.17, using type/token ratios, the dependency entropy of syntactic neighbours, and boundary rates. All three traits hold across every language sampled. They then train transformer LMs, with n-gram baselines, on counterfactual English variants in which each trait is altered. Syntactic generalization is scored on BLiMP.

Learning is best when all three traits co-occur. Frequency shows a Goldilocks pattern: function words must be common enough to be reliable but varied enough to stay informative. Attention probing and ablation show that models trained on natural-like input develop heads specialized for function words and rely on them more.

The work links acquisition theory with LM training. It also supports viewing function words as learnability anchors shaped by cultural evolution.

## Key points
- The corpus study covers 339 UD treebanks in 186 languages. In every one, function words have a small type share but a large token share, lower dependency entropy than content words, and higher phrase-boundary rates.
- Counterfactual languages manipulate three dimensions:
  - frequency: NoFunction, MoreFunction, FiveFunction, StandardFunction;
  - structural dependency: RandomDep, BigramDep, PhraseDependency;
  - boundary placement: WithinBoundary vs. AtBoundary.
- English was the only language modeled, because the design needs reliable parsing (Stanza) and a standard syntactic benchmark (BLiMP). Training data came from Wikipedia.
- Breaking reliable structural association hurt learning more than disrupting boundary alignment or frequency. Even a 5-gram model scores above chance on BLiMP, so the authors treat the benchmark only as a proxy.
- Code is on GitHub (picol-georgetown/function_word) and trained models are in a Hugging Face collection (xiulinyang/function-words). Bound grammatical morphology (e.g., Turkish suffixes) and prosody are left for future work.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Xiulin Yang, Heidi R. Getz and Ethan Gotlieb Wilcox. 2026. Function Words as Statistical Cues for Language Learning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yang2026function,
  title = {{Function Words as Statistical Cues for Language Learning}},
  author = {Xiulin Yang, Heidi R. Getz and Ethan Gotlieb Wilcox},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.728},
  url = {https://doi.org/10.18653/v1/2026.acl-long.728}
}
```

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*Updated 31 October 2026*