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Embedded scalars

Paper · 2013 · Bart Geurts and Bob van Tiel

Summary

Geurts and van Tiel ask how scalar words like some get an "upper-bounded" reading (some but not all) when they sit inside other operators: belief verbs, quantifiers, disjunctions and similar. They introduce the neutral label UBC (upper-bounded construal) and argue that both camps already accept two sources for UBCs:

  • Conversational implicature, which works only at the level of a whole utterance.
  • Truth-conditional narrowing, which can occur inside embeddings.

The real dispute is how often each one applies. Conventionalists (lexicalist or grammatical, e.g. a covert only operator) predict embedded UBCs everywhere. Griceans predict they are marked and rare, needing triggers such as contrastive stress.

Since introspection only shows that a reading is possible, the authors review the experimental record. They reanalyse the raw data of Chemla & Spector and run their own follow-up. They find that studies favouring conventionalism nudged participants through contrasts between or within items and through heavy repetition. Those studies do reveal typicality effects for embedded scalars, but they give no evidence that embedded UBCs arise freely. The authors conclude that the evidence supports a Gricean account.

Key points

  • A standard quantity implicature for Julius believes that some goats are happy gives only the weak inference that the speaker thinks Julius doesn't believe all. The stronger reading (Julius believes not all) follows once the common ground assumes Julius has an opinion either way.
  • The authors sort accounts of narrowing into three families:
    • lexicalist (Levinson, Chierchia 2004);
    • grammatical, as developed in Chierchia's 2006 work, Fox's 2007 work and the joint 2012 paper by Chierchia, Fox & Spector;
    • pragmatic (Horn, Noveck & Sperber, Geurts).
  • In their own experiment, participants rated pictures on a 7-point scale; items were randomised across 9 lists, and 3 non-native speakers were excluded.
    • Group B (high contrast): True 80%, HiCon 64%, Literal 49%, False 32%. HiCon differed significantly from False (t(25) = 4.035, p < .001).
    • Group A (low contrast): True 84%, Literal 74%, LoCon 37%, False 30%. LoCon did not differ from False (t(24) = 1.252, p = .22).
  • In Chemla & Spector's study, both picture types scored about 73%. The authors attribute this to knock-on effects from repetition: each participant saw the critical sentence 52 times.
  • The embedded scalar data are better explained as typicality effects than as freely available local enrichment.

Interesting ideas

  • I1. Use a descriptive, mechanism-neutral label (UBC) for an observed reading, kept separate from the theory of what produces it; this prevents conflating the output with its cause.
  • I2. Treat the literal, lower-bounded meaning as the default and allow embedded narrowing only when a marked trigger is present; this blocks over-enrichment in contexts where it is unlicensed.
  • I3. Derive a seemingly "embedded" inference globally by combining the weak implicature with a background assumption (here, that the believer holds an opinion one way or the other). This keeps the inference explainable step by step.
  • I4. Check experimental items for contrasts between or within items and for repetition that could prime the hypothesis being tested; the same biases can leak into any benchmark.
  • I5. Graded acceptability can reflect how typical a situation is rather than whether a sentence is true or false; truth judgements and best-description judgements should be kept apart.

For Mova

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

  • I1 (frames): Tag some → not all readings in prag with one neutral flag, plus a separate field recording the source (utterance-level implicature vs. local narrowing licensed by stress or contrast).
  • I2 (frames): In prag, compute scalar implicatures only at the speech-act level, and narrow a scalar inside a belief verb, conditional or quantifier only when the tree or context shows explicit contrast.
  • I3 (world): Store an "opinionated agent" assumption about belief holders in the text world, so that a weak implicature plus this assumption yields the stronger nested belief through an auditable chain.
  • I4 (learn): When building evaluation sets for implicature, add low-contrast controls and avoid repeating near-identical items, so that a model's "enriched" answers cannot be explained by priming.
  • I5 (answer): Have readers separate "is this literally true?" from "is this a good description?" when answering questions about pictures or stories involving some/all.

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

🎓Radboud University NijmegenNijmegen · Netherlands20 works · 1,688 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Implicature · 138 works

Cite

Cite as Bart Geurts and Bob van Tiel. 2013. Embedded scalars. Semantics and Pragmatics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.3765/sp.6.9

@article{geurts2013embedded,
  title = {{Embedded scalars}},
  author = {Bart Geurts and Bob {van Tiel}},
  journal = {Semantics and Pragmatics},
  year = {2013},
  doi = {10.3765/sp.6.9},
  url = {https://doi.org/10.3765/sp.6.9}
}

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