---
title: "Making sense of spoken plurals — Shafaei-Bajestan 2022"
description: "It compares two candidate operations. 52 references. Among them: 0."
authors: ["Elnaz Shafaei-Bajestan", "Peter Uhrig", "R. Harald Baayen"]
year: 2022
venue: "arXiv:2207.01947"
source: "https://arxiv.org/abs/2207.01947"
doi: "10.48550/arxiv.2207.01947"
arxiv: "2207.01947"
licence: "CC BY-SA 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/shafaei-bajestan-2022-making-sense-spoken-plurals/"
---
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# Making sense of spoken plurals

**Authors:** [Elnaz Shafaei-Bajestan](https://md.research.elamur.ai/free/en/authors/elnaz-shafaei-bajestan/), [Peter Uhrig](https://md.research.elamur.ai/free/en/authors/peter-uhrig/) and [R. Harald Baayen](https://md.research.elamur.ai/free/en/authors/r-harald-baayen/) · **Year:** 2022 · **Venue:** arXiv:2207.01947 · **Licence:** [CC BY-SA 4.0 (arXiv)](http://creativecommons.org/licenses/by-sa/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2207.01947) · [PDF](https://arxiv.org/pdf/2207.01947) · [DOI 10.48550/arxiv.2207.01947](https://doi.org/10.48550/arxiv.2207.01947) · [arXiv 2207.01947](https://arxiv.org/abs/2207.01947) · [OpenAlex](https://openalex.org/W4284709175)

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

## Summary
The paper asks how the meaning of an English plural noun should be derived from the meaning of its singular in distributional semantics. It compares two candidate operations. FRACSS learns a single linear map from all singular–plural embedding pairs. Cosine Class Average (CCA) adds to the singular vector an average shift computed only within the noun's WordNet semantic class. To choose between them, the authors test which set of predicted plural vectors fits better with how plurals actually sound. They take audio tokens of plurals from the NewsScape corpus of spoken American English and build form vectors from the signal. They then measure two things: how accurately a Discriminative Lexicon Model mapping recovers meaning from form, and how strongly pairwise form distances track pairwise meaning distances. CCA comes out ahead on both measures, on training and held-out data alike. The broader claim is that [+plural] works less like one abstract rule and more like a set of mid-level, class-bound generalizations. This matters for theories of morphology and of lexical processing.

## Key points
- A t-SNE projection of word2vec shift vectors for 11,749 singular–plural pairs shows clusters by semantic class (persons, animals, communication words). CCA therefore uses around 400 classes derived from WordNet.
- FRACSS (from Marelli & Baroni, 2015) solves SM = P over all pairs. CCA extends the average-shift approach of Kisselew et al. (2015) to class-specific shifts. Each model leaves a word-specific residual vector for known plurals.
- Form data come from audio tokens in NewsScape, with both audio-based and phone-based distance measures. Only nouns that take {S} as the plural exponent are included.
- CCA plural vectors are predicted more precisely from audio, for both training and test data. Randomly re-pairing forms and meanings lowers accuracy more for CCA than for FRACSS.
- In a model-free analysis following Monaghan et al. (2014), correlations between form distance and meaning distance are higher with CCA. The authors note that CCA receives extra class information and is arguably the more complex model.

## Interesting ideas
- Plural meaning shifts differently by semantic class, because multiples of fruit, animals or people occur in very different configurations, so a class-conditioned operation beats one global transform.
- Two meaning representations can be ranked by how well each aligns with an independent signal, here the spoken form, instead of only by closeness to gold vectors.
- Shuffling form–meaning pairs and measuring the accuracy drop works as a negative control: a bigger drop means the representation carries more real structure.
- A pairwise distance-correlation test checks form–meaning alignment without depending on any trained mapping.
- Each known plural is modeled as a predictable part plus a residual capturing its own collocational behavior.

## For Mova

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

- **I1** (world): when a plural entity enters the world of a text, its default grouping or configuration could be chosen per WordNet class rather than from one generic "set of X" template.
- **I2** (learn): competing frame or role analyses could be scored by agreement with an independent witness, such as a separate parser or the surface tree, rather than by self-consistency.
- **I3** (learn): randomized pairings of sentences and frames could serve as a standard baseline, showing whether a learned mapping exploits genuine form–meaning regularity.
- **I4** (back): the text → graph → text compressor could be audited by correlating distances between source sentences with distances between their meaning graphs.
- **I5** (tokens): morphology could produce a rule-based default reading from lemma plus number and keep a small per-word residual entry for plurals whose usage drifts from that default.

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

## Institutions

- [University of Tübingen](https://md.research.elamur.ai/free/en/universities/university-of-tubingen/) — University, Tübingen, Germany · 75 works · 41,081 citations
- [Friedrich-Alexander-Universität Erlangen-Nürnberg](https://md.research.elamur.ai/free/en/universities/friedrich-alexander-universitat-erlangen-nurnberg/) — University, Erlangen, Germany · 8 works · 48 citations

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## Cite

Cite as: Elnaz Shafaei-Bajestan, Peter Uhrig and R. Harald Baayen. 2022. Making sense of spoken plurals. arXiv:2207.01947.

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

```bibtex
@misc{shafaeibajestan2022making,
  title = {{Making sense of spoken plurals}},
  author = {Elnaz Shafaei-Bajestan, Peter Uhrig and R. Harald Baayen},
  year = {2022},
  doi = {10.48550/arxiv.2207.01947},
  eprint = {2207.01947},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2207.01947}
}
```

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## References

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