---
title: "Uniform Information Density and Syntactic Reduction: Revisiting"
description: "The paper re-tests a classic Uniform Information Density (UID) result: English speakers tend to drop the… 68 references, cited by 1 paper here."
authors: ["Hailin Hao", "Elsi Kaiser"]
year: 2025
venue: "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2025.emnlp-main.1117/"
doi: "10.18653/v1/2025.emnlp-main.1117"
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/hao-2025-uniform-information-density-syntactic-reduction/"
---
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# Uniform Information Density and Syntactic Reduction: Revisiting \*that\*-Mentioning in English Complement Clauses

**Authors:** [Hailin Hao](https://md.research.elamur.ai/free/en/authors/hailin-hao/) and [Elsi Kaiser](https://md.research.elamur.ai/free/en/authors/elsi-kaiser/) · **Year:** 2025 · **Venue:** [Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/)) · **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/2025.emnlp-main.1117/) · [PDF](https://aclanthology.org/2025.emnlp-main.1117.pdf) · [DOI 10.18653/v1/2025.emnlp-main.1117](https://doi.org/10.18653/v1/2025.emnlp-main.1117) · [OpenAlex](https://openalex.org/W4416036078)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/hao-2025-uniform-information-density-syntactic-reduction/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper re-tests a classic Uniform Information Density (UID) result: English speakers tend to drop the optional complementizer *that* when the following complement clause (CC) is predictable. They tend to keep it when the CC is surprising.

The original evidence (Jaeger, 2010) had two weaknesses:
- It used a small, older Switchboard sample.
- It measured predictability with a static per-verb subcategorization rate.

The authors instead use CANDOR, a large corpus of recent spontaneous Zoom conversations parsed with spaCy into UD trees. They train binary classifiers that predict whether a CC follows a matrix verb. These classifiers use either hand-picked linguistic features or GPT-2 contextual embeddings of the verb computed from left context only. The resulting probabilities become information-density predictors in models of *that*-mentioning.

The core UID effect replicates. However, the subcategorization-based measure loses its effect once verb identity is controlled, which suggests it mostly encodes lexical quirks. The embedding-based measure stays significant. This shows that richer contextual representations give a better estimate of structural predictability, and that combining large naturalistic corpora with NLP tools can sharpen psycholinguistic tests.

## Key points
- **Data:** CANDOR (Reece et al., 2023) contains 1,656 dyadic conversations from 1,456 participants, about 8 million words. It was segmented with the Cliffhanger algorithm and dependency-parsed with spaCy following Universal Dependencies conventions.
- **Verb set:** The authors began with an inventory of 86 CC-taking verbs compiled from Jaeger and Grimshaw (2013) and Jaeger (2010). They kept the 50 most frequent (at least 100 occurrences).
- **Training data:** 236,504 verb instances, each labeled for whether a CC follows (33.01% positive). After cleaning, the analysis covers over 50,000 CCs. Jaeger's study had roughly 8,000 CCs and 31 verbs.
- **Embedding features:** GPT-2 Small final hidden state of the matrix verb token, using only pre-CC context. The 768 dimensions were reduced to 50 with PCA, keeping more than 99% of the variance. These embeddings beat all hand-selected features, including subcategorization probability, at predicting CC presence.
- **Other effects on *that*-mentioning:**
  - Availability: rarer verbs and rarer CC onsets lead to more *that*.
  - Syntactic priming from the previous CC.
  - Speaker commitment: higher commitment leads to less *that*.
  - Avoidance of *that-that* sequences.
  - Disfluency: fillers and repetitions lead to more *that*.
  - Dependency-locality evidence was mixed.

## Interesting ideas
- Upcoming syntactic structure can be predicted from the contextual embedding of a single head word computed only on its left context, which turns a static lexical statistic into a context-sensitive one.
- To check whether a predictor captures a general effect or merely memorizes per-word idiosyncrasies, add a per-lemma random intercept and see whether the predictor's effect survives.
- A verb's subcategorization preference can be estimated by labeling every occurrence of the verb in an automatically UD-parsed corpus by whether a target dependent follows it.
- Feature sets were chosen by adding features one at a time and keeping each only if both AIC and BIC improve, with Lasso as a cross-check.
- Including or dropping an optional function word is governed by several measurable pressures: predictability, priming, avoidance of repeated forms, and speaker commitment.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tree): A left-context "will this verb take a clausal complement?" score could serve as a feature or tiebreaker when gparse or the perceptron parser decides whether a *that*-less clause attaches as ccomp.
- **I2** (learn): Evaluation of repair rules or teacher-derived features could include a per-lemma baseline as a negative control, so gains that only encode lexical identity are not mistaken for real generalization.
- **I3** (world): Verb-class knowledge could be enriched with corpus-derived frame frequencies (ccomp vs. obj vs. other) computed from Mova's own parsed silver data.
- **I4** (learn): This gives an explainable, parsimonious way to decide which hand-crafted signals a small classifier really needs, in line with Mova's no-black-box goal.
- **I5** (back): When generating a complement clause from the meaning graph, the generator could decide on overt *that* from these factors, such as avoiding *that that*, keeping it after rare verbs, and repeating the previous choice, to produce more natural text.

*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: Hailin Hao and Elsi Kaiser. 2025. Uniform Information Density and Syntactic Reduction: Revisiting \*that\*-Mentioning in English Complement Clauses. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{hao2025uniform,
  title = {{Uniform Information Density and Syntactic Reduction: Revisiting *that*-Mentioning in English Complement Clauses}},
  author = {Hailin Hao and Elsi Kaiser},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.1117},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.1117}
}
```

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*Updated 2 November 2026*