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# Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

**Authors:** Paloma Jeretic, [Alex Warstadt](https://md.mova.elamur.ai/free/en/docs/papers/authors/alex-warstadt/), Suvrat Bhooshan, Adina Williams · **Year:** 2020 · **Venue:** ACL 2020 **Link:** [aclanthology.org/2020.acl-main.768](https://aclanthology.org/2020.acl-main.768/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); the dataset's own licence must be checked before any use beyond tests

[**77** citations](https://openalex.org/works?filter=cites:W3015830489) openalex · 2026-10-18[**86** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/jeretic-2020-are-natural-language-inference-models/) of the 140673 papers whose bibliographies we read[Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics](https://aclanthology.org/volumes/2020.acl-main/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2020.acl-main.768/)
- [PDF](https://aclanthology.org/2020.acl-main.768.pdf)
- [DOI 10.18653/v1/2020.acl-main.768](https://doi.org/10.18653/v1/2020.acl-main.768)
- [arXiv 2004.03066](https://arxiv.org/abs/2004.03066)
- [OpenAlex](https://openalex.org/W3015830489)

**Authors (4)**

1. [Paloma Jeretic](https://openalex.org/A5029729156) [ORCID](https://orcid.org/0000-0003-4680-8577) · h 5 · 223 citations · 33 works
2. [Alex Warstadt](https://openalex.org/A5089452138) [ORCID](https://orcid.org/0000-0002-5397-3151) · h 16 · 2412 citations · 126 works
3. [Suvrat Bhooshan](https://openalex.org/A5054365077) h 6 · 313 citations · 10 works
4. [Adina Williams](https://openalex.org/A5062696000) [ORCID](https://orcid.org/0000-0001-5281-3343) · h 29 · 10141 citations · 154 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

IMPPRES is a template-generated diagnostic set of more than 25,000 premise–hypothesis pairs, built with the BLiMP generator. The scalar implicature part covers six scale types (some/all, or/and, can/have to, numerals, scalar adjectives, scalar verbs). Each pair carries both a "logical" and a "pragmatic" label, plus negation and opposite controls. The presupposition part covers eight trigger families (possessed definites, both, change of state, cleft existence, cleft uniqueness, only, all N, questions), and each trigger is placed under negation, a modal, a question and a conditional to test projection. BERT fine-tuned on MultiNLI passes the controls. It reads «some» pragmatically as «not all» more often than logically, but treats scalemates such as *or/and* and *good/excellent* as synonyms. It also recognises projected presuppositions for *only*, clefts, possessives and questions even under negation. MultiNLI itself contains almost no lexically triggered pragmatic pairs.

## Ideas

- I1. Write a projection test paradigm for `prag/src/infer.rs` after the IMPPRES design, with our own sentences: each trigger appears plain, negated, under a modal, in a yes/no question and in an «if» clause. The presupposition must be produced in all five; a scalar implicature must be produced only in the plain affirmative. Today the implicature code checks negation and cancellation, but nothing tests that presuppositions project through questions and conditionals. Each failing cell is a concrete bug.
- I2. Add the trigger families Mova lacks to `infer::presuppositions`: possessive («Jo's cat» → Jo has a cat; `nmod:poss`), *only* («only X V» → X V), cleft («It was X who V» → someone V), *both* (exactly two), *all N* (N exist), and wh-questions («Who left?» → someone left). Each one is a tree pattern with a stated trigger, and each needs a negative control.
- I3. Keep scale direction explicit. BERT's error was treating scalemates as synonyms, and Mova's `SCALES` table is directional by construction. Add a test that «Jo ate all of the cake» yields no «not some» inference and that the reverse pair (*all* → *some* holds) is an entailment, not an implicature. The `infer` explanation should name which one it is.
- I4. The double labelling (logical vs pragmatic) suits the frame student. A remembered «some X» frame should support a question option «not all X» only as an implicature, which is cancellable, and that weight should be lower than for an entailment in `world/src/knowledge.rs`. Count the course questions where this matters before changing anything.

## For Mova

[33. Implicature and presupposition](https://md.mova.elamur.ai/free/en/docs/languages/en/33-implicature-and-presupposition/) §4, §6.1–6.2 and §7.1 (scalar implicatures, triggers, projection); `prag/src/infer.rs`; measure: a pass/fail table of the projection paradigm (triggers × five environments), run by `cargo test -p prag`.

Topics: [Implicature](https://md.mova.elamur.ai/free/en/docs/papers/topics/implicature/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2020.acl-main.768](https://doi.org/10.18653/v1/2020.acl-main.768) · [arXiv 2004.03066](https://arxiv.org/abs/2004.03066)

```
@inproceedings{jeretic2020are,
  title = {{Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition}},
  author = {Jeretic, Paloma and Warstadt, Alex and Bhooshan, Suvrat and Williams, Adina},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.768},
  eprint = {2004.03066},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2020.acl-main.768}
}
```

- [← previous The syntax of comparison constructions in diachronic and dialectal perspective](https://md.mova.elamur.ai/free/en/docs/papers/jager-2019-syntax-comparison-constructions-diachronic-dialectal/)
- [next → Negation in English and Other Languages](https://md.mova.elamur.ai/free/en/docs/papers/jespersen-1917-negation-english-other-languages/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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