---
title: "Rational Communication Shapes Morphological Composition"
description: "The paper asks why English settled on one particular morpheme combination for a new word when other combinations, built… 34 references."
authors: ["Fengyuan Yang", "Yongqian Peng", "Yuxi Ma", "Chenheng Xu", "Yixin Zhu"]
year: 2026
venue: "arXiv:2605.03510"
source: "https://arxiv.org/abs/2605.03510"
doi: "10.48550/arxiv.2605.03510"
arxiv: "2605.03510"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yang-2026-rational-communication-shapes-morphological-composition/"
---
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# Rational Communication Shapes Morphological Composition

**Authors:** Fengyuan Yang, Yongqian Peng, Yuxi Ma, Chenheng Xu and [Yixin Zhu](https://md.research.elamur.ai/free/en/authors/yixin-zhu/) · **Year:** 2026 · **Venue:** arXiv:2605.03510 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2605.03510) · [PDF](https://arxiv.org/pdf/2605.03510) · [DOI 10.48550/arxiv.2605.03510](https://doi.org/10.48550/arxiv.2605.03510) · [arXiv 2605.03510](https://arxiv.org/abs/2605.03510) · [OpenAlex](https://openalex.org/W7160428698)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper asks why English settled on one particular morpheme combination for a new word when other combinations, built from morphemes that existed at the time, could have named the same concept. The authors treat word formation as a cooperative game in the Rational Speech Act (RSA) framework. A pragmatic speaker scores candidate morpheme sequences by how easily a literal listener could recover the intended concept, minus how costly the form is to produce. The data are 4323 WordNet compounds and derivations whose first appearance, taken from COHA/COCA counts, falls between 1820 and 2019. A time-indexed lexicon ensures that only morphemes available at the coinage year compete. The authors rank each real word against generated alternatives. Models that combine recoverability with cost beat semantics-only and cost-only baselines on MRR and Acc@k. The gain over the semantic baseline widens as the candidate pool grows, because meaning alone stops discriminating. The work brings rational-communication accounts down to the internal structure of words and gives an interpretable model of lexical choice.

## Key points
- **Dataset construction:**
  - WordNet supplies the items and their glosses.
  - MorphSeg provides gold morpheme segmentation.
  - Emergence year is the first non-zero year in COHA/COCA.
  - The CMU Pronouncing Dictionary supplies phoneme and syllable counts.
  - Affixes.org extends the morpheme inventory.
- **Semantic side:** each candidate is described by five statistics of morpheme-to-concept similarity, computed with Qwen3 embeddings:
  - mean, max, standard deviation and entropy of the per-morpheme similarities;
  - the similarity of the whole candidate to the target.
  
  A kNN gate around the target gloss discards candidates containing out-of-neighbourhood morphemes before ranking.
- **Cost side:** a learned function maps each morpheme's frequency, phonological complexity and length to a cost, and these costs are summed over the morphemes. The two scores are combined in two ways:
  - Linear S1, with learnable weights α, β and b;
  - Nonlinear S1, a small MLP.
- **Results:**
  - S1 models beat the single-factor baselines, and cost-only is weakest.
  - An unconstrained discriminative model over the full 13-dimensional feature vector performs best overall.
  - The model ordering holds across cumulative training windows ending 1830–1910.
- **Limitations:**
  - Candidates are treated as unordered sets, ignoring morphotactics and head–modifier order.
  - Embeddings are contemporary, so historical meaning shift is only indirectly captured.
  - The evaluation covers English only.

## Interesting ideas
- Judge a choice model by where the actually attested form lands among the alternatives available at that moment, using MRR and Acc@k instead of plain accuracy; this works for any task with a single observed choice.
- Describe a multi-part candidate by summary statistics of how each part relates to the target, keeping part-level evidence visible rather than pooling everything into one vector.
- Apply a hard neighbourhood gate before scoring, so the expensive ranker only sees semantically plausible candidates and the exclusion reason is explicit.
- Model a chosen form as listener recoverability traded against speaker cost, compared over an explicit set of alternatives.
- Pair the interpretable two-factor model with an opaque full-feature model used only as a ceiling, and read the gap between them as a map of missing interactions.

## For Mova

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

- **I1** (back): in generation and the text → graph → text compressor, rank the original wording among candidate paraphrases produced from the same graph, to measure lexical-choice quality explainably.
- **I2** (tokens): for unseen compounds and derivations, score competing segmentations by per-morpheme similarity to a candidate gloss, so the lemma and morphology decision can cite which part carried the meaning.
- **I3** (world): when grounding an unknown word into the WordNet/Wikidata ontology, consider only concepts near its context or gloss, and log why others were rejected.
- **I4** (frames): in prag, compute implicatures by asking which cheaper or more informative alternatives the speaker passed over, and record the comparison as the explanation.
- **I5** (learn): train a black-box diagnostic beside rule-based components to locate the feature interactions that explainable repair rules still miss.

*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: Fengyuan Yang, Yongqian Peng, Yuxi Ma, Chenheng Xu and Yixin Zhu. 2026. Rational Communication Shapes Morphological Composition. arXiv:2605.03510.

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

```bibtex
@misc{yang2026rational,
  title = {{Rational Communication Shapes Morphological Composition}},
  author = {Fengyuan Yang, Yongqian Peng, Yuxi Ma, Chenheng Xu and Yixin Zhu},
  year = {2026},
  doi = {10.48550/arxiv.2605.03510},
  eprint = {2605.03510},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2605.03510}
}
```

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*Updated 1 November 2026*