---
title: "Pragmatic Modelling in Language Learning — Bala 2026"
description: "The paper investigates how adult caregivers respond to a child's early attempts to construct question–answer (QA) pairs and whether this response changes…"
authors: ["Maryam Bala", "Johannes Heim", "Elspeth Edelstein", "Arabella Sinclair"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.897/"
doi: "10.63317/4g9ggjcrgfax"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/bala-2026-pragmatic-modelling-language-learning-caregiver/"
---
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# Pragmatic Modelling in Language Learning: Caregiver Question-Answer Feedback in Child-Directed Dialogue

**Authors:** Maryam Bala, Johannes Heim, Elspeth Edelstein, Arabella Sinclair · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.897/) · [PDF](https://aclanthology.org/2026.lrec-1.897.pdf) · [DOI 10.63317/4g9ggjcrgfax](https://doi.org/10.63317/4g9ggjcrgfax) · [OpenAlex](https://openalex.org/W7160195065)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
The paper investigates how adult caregivers respond to a child's early attempts to construct question–answer (QA) pairs and whether this response changes as the child's language develops. The authors adopt their own taxonomy of four feedback types. Two of them belong to "instructions": Reformulating (the adult rephrases an ill-formed child question) and Affirming (the adult confirms the child's utterance). The other two belong to "demonstrations": Exemplifying (the adult repeats the child's question and answers it themselves) and Modelling (the adult, on their own initiative, demonstrates a complete QA sequence). Experts manually annotated dialogues from CHILDES. Using these data, the authors trained automatic classifiers to extend the analysis to a broader portion of the corpus. It turned out that the emergence, frequency and complexity of each type change systematically with the child's age. This resembles an implicit "curriculum" of pragmatic development. Large language models acting as judges perform poorly at distinguishing these fine-grained categories. The annotated dataset has been released openly. It is useful for research on pragmatics, curriculum learning, and training language models on developmentally realistic feedback signals.

## Key points
- **Taxonomy:** two groups, Demonstration (Modelling, Exemplifying) and Instruction (Reformulating, Affirming). Depending on the context, a single utterance may belong to several types simultaneously.
- **Developmental analysis:** the data are divided into age groups (\<20, \<30, \<40, \<50 months). For each group, MLU, lexical complexity (AoA), part-of-speech proportions, lexical overlap and structural distance (Levenshtein distance between POS sequences) were compared. Significance was tested using t-tests with Bonferroni correction and Pearson correlations with age.
- **LLM-as-a-judge:** Llama3 8b-Instruct performed best, with a mean accuracy of about 0.61–0.62 under any of the three prompt variants (definitions only, definitions with positive examples, definitions with positive and negative examples). Gemma 2b-Instruct and Falcon 7b-Instruct achieved roughly 0.46–0.51, i.e., chance level. They often labelled almost all examples as positive (recall ≈ 1.00).
- **Automatic annotation:** a BERT-based model was compared with logistic regression on semantic and information-theoretic features. BERT and mBERT were also tested with and without dialogue context. For each type, "easy" and "hard" examples were evaluated separately (hard ones being those that are ambiguous for a human annotator). Performance is mostly lower on hard examples, and for Affirming it is lower in all configurations.
- **Resource:** the corpus and analysis code are available on GitHub: `the-context-lab/childQAfeedback`. The paper is published in the proceedings of LREC 2026.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cite

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

```bibtex
@inproceedings{bala2026pragmatic,
  title = {{Pragmatic Modelling in Language Learning: Caregiver Question-Answer Feedback in Child-Directed Dialogue}},
  author = {Bala, Maryam and Heim, Johannes and Edelstein, Elspeth and Sinclair, Arabella},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4g9ggjcrgfax},
  url = {https://doi.org/10.63317/4g9ggjcrgfax}
}
```

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