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# Predicting scalar inferences from "or" to "not both" using neural sentence encoders

**Authors:** Elissa Li, [Sebastian Schuster](https://md.mova.elamur.ai/free/en/docs/papers/authors/sebastian-schuster/), [Judith Degen](https://md.mova.elamur.ai/free/en/docs/papers/authors/judith-degen/) · **Year:** 2021 · **Venue:** Society for Computation in Linguistics (SCiL 2021) **Link:** [aclanthology.org/2021.scil-1.54](https://aclanthology.org/2021.scil-1.54/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**5** citations](https://openalex.org/works?filter=cites:W3133527511) openalex-snapshot · 2026-10-14[**10** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/li-2021-predicting-scalar-inferences-or-not/) of the 140673 papers whose bibliographies we read[Proceedings of the Society for Computation in Linguistics 2021](https://aclanthology.org/volumes/2021.scil-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2021.scil-1.54/)
- [PDF](https://aclanthology.org/2021.scil-1.54.pdf)
- [DOI 10.7275/xr01-a852](https://doi.org/10.7275/xr01-a852)
- [OpenAlex](https://openalex.org/W3133527511)

**Authors (3)**

1. [Elissa Li](https://openalex.org/A5050719822) [Semantic Scholar](https://www.semanticscholar.org/author/1648314890) · h 1 · 6 citations · 2 works
2. [Sebastian Schuster](https://openalex.org/A5018467280) [ORCID](https://orcid.org/0000-0002-8066-0142) · [Semantic Scholar](https://www.semanticscholar.org/author/145157639) · h 16 · 2524 citations · 48 works
3. [Judith Degen](https://openalex.org/A5054627290) [ORCID](https://orcid.org/0000-0003-2513-0234) · [Semantic Scholar](https://www.semanticscholar.org/author/38548904) · h 25 · 2033 citations · 229 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

A short paper extending the «some → not all» strength studies (Degen 2015; Schuster et al. 2020) to «or → not both». 1,244 Switchboard sentences with *or* were rated, in context, for how close they are to the same sentence with «but not both» added, on a slider and on a 1–7 scale. Strength varies widely and individual items often have bimodal ratings: people disagree on whether exclusivity was meant. Two features matter. *either* strengthens the inference («You're either liberal or conservative», 6.6 of 7). A downward-entailing context such as negation kills it («I don't believe in drugs or alcohol», 1.1). Together the features explain much less variance than for *some* (marginal R² 0.07). The authors read this as evidence that *or* often has other jobs — definitional, metalinguistic, listing — where «and» is not a salient alternative. A BERT+BiLSTM model predicting a discrete rating distribution reaches only r = 0.47.

## Ideas

- I1. Keep «or → not both» in `prag/src/infer.rs` (the `or < and` scale) but gate it more strictly than «some». Fire only with *either … or*, or with two clausal or eventive disjuncts in an assertion. Never fire under negation or another downward-entailing operator (*no, never, without, if*-clause, question). Add tests: «I don't believe in drugs or alcohol» → no inference; «either X or Y» → strong.
- I2. Recognise the non-exclusive uses of *or* before inferring: definitional or appositive *or* («the gnu, or wildebeest») and approximate numbers («two or three days»). Mark them as `or`-not-scalar in the explanation. Both are frequent in school lessons and tales, and both would otherwise yield false «not both» claims.
- I3. Because people themselves split on many *or* items, do not train a strength classifier for *or* on teacher labels. Treat the rule's output as a hint the explanation states, and record the measured disagreement as a limit in chapter 33 §4.2.

## For Mova

[33. Implicature and presupposition](https://md.mova.elamur.ai/free/en/docs/languages/en/33-implicature-and-presupposition/) §4.1–4.2; `prag/src/infer.rs` (`implicatures`, `SCALES`); measure: unit tests with negative controls, and a count of *or* inferences on lesson texts before and after the gate (with a checked sample).

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.7275/xr01-a852](https://doi.org/10.7275/xr01-a852)

```
@inproceedings{li2021predicting,
  title = {{Predicting scalar inferences from "or" to "not both" using neural sentence encoders}},
  author = {Li, Elissa and Schuster, Sebastian and Degen, Judith},
  booktitle = {Proceedings of the Society for Computation in Linguistics 2021},
  year = {2021},
  doi = {10.7275/xr01-a852},
  url = {https://doi.org/10.7275/xr01-a852}
}
```

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## References

14 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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