[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/hu-2023-expectations-over-unspoken-alternatives-predict/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [English](https://md.mova.elamur.ai/free/en/docs/papers/en/) / Expectations over Unspoken Alternatives Predict Pragmatic Inferences

# Expectations over Unspoken Alternatives Predict Pragmatic Inferences

**Authors:** [Jennifer Hu](https://md.mova.elamur.ai/free/en/docs/papers/authors/jennifer-hu/), [Roger Levy](https://md.mova.elamur.ai/free/en/docs/papers/authors/roger-levy/), [Judith Degen](https://md.mova.elamur.ai/free/en/docs/papers/authors/judith-degen/), [Sebastian Schuster](https://md.mova.elamur.ai/free/en/docs/papers/authors/sebastian-schuster/) · **Year:** 2023 · **Venue:** Transactions of the Association for Computational Linguistics 11, 885–901 **Link:** [aclanthology.org/2023.tacl-1.50](https://aclanthology.org/2023.tacl-1.50/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**13** citations](https://openalex.org/works?filter=cites:W4385267227) openalex-snapshot · 2026-10-14[**14** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hu-2023-expectations-over-unspoken-alternatives-predict/) of the 140673 papers whose bibliographies we read[Transactions of the Association for Computational Linguistics](https://aclanthology.org/volumes/2023.tacl-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2023.tacl-1.50/)
- [PDF](https://aclanthology.org/2023.tacl-1.50.pdf)
- [DOI 10.1162/tacl_a_00579](https://doi.org/10.1162/tacl_a_00579)
- [arXiv 2304.04758](https://arxiv.org/abs/2304.04758)
- [OpenAlex](https://openalex.org/W4385267227)

**Authors (4)**

1. [Jennifer Hu](https://openalex.org/A5067533298) [ORCID](https://orcid.org/0000-0002-0021-5699) · [Semantic Scholar](https://www.semanticscholar.org/author/2112327566) · h 52 · 9247 citations · 339 works
2. [Roger Levy](https://openalex.org/A5090215557) [ORCID](https://orcid.org/0000-0002-4493-8864) · [Semantic Scholar](https://www.semanticscholar.org/author/50007746) · h 55 · 23036 citations · 343 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
4. [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

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

## What it is about

Scalar inferences ("some" implies "not all") vary a lot from one instance to another, and across scales such as "good, excellent". The authors test whether the expectedness of the stronger term, given the context, predicts that variation. They use GPT-2 and BERT surprisal as stand-ins for human predictions. Within "some, all" (Degen 2015, 1363 sentences) the concept-based predictor, a similarity-weighted average surprisal over a fixed set of quantifiers, correlates with human ratings (Pearson −0.432) and stays significant in the full regression (coefficient −0.782, p < 0.001). The raw string-based surprisal does not (coefficient 0.008, p = 0.960). Across 148 scales from four datasets, the concept-based predictor is significant in three of four datasets (for example r = −0.400 for Ronai and Xiang); van Tiel et al.'s data, with the fewest participants, show no effect. The paper argues that alternatives behave as concepts, not as word forms.

## Ideas

- I1. `prag/src/infer.rs`: a scalar-implicature rule for "some" that consults a fixed alternative set (each, every, few, half, much, many, most, all), as the paper does, and uses the expectedness of "all" in the sentence. Replace the language model by counts: the frequency of "X, but not Y" in a corpus the project already holds, computed with BTreeMap counts.
- I2. Weight the alternatives by a deterministic similarity (for example shared lexicon entries) rather than GloVe cosine, and measure whether the weighted score still predicts the paper's ratings.
- I3. Add a world-prior rule for implausible combinations (a movie that both starts and finishes at 2:30), since the paper notes that priors can produce "yes" answers without pragmatic reasoning.

## For Mova

The chapter is [33. Implicature and presupposition](https://md.mova.elamur.ai/free/en/docs/languages/en/33-implicature-and-presupposition/) (scalar implicature) and [10. Word classes](https://md.mova.elamur.ai/free/en/docs/languages/en/10-word-classes/) (quantifiers). The code is `prag/src/infer.rs` and `prag/src/rsa.rs`. Measures: Spearman correlation between the count-based expectedness score and the paper's published ratings (in the paper's figures), with the random-alternative control; run twice. No neural model is used in Mova; the paper is the source of the finding and of the design.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/tacl_a_00579](https://doi.org/10.1162/tacl_a_00579) · [arXiv 2304.04758](https://arxiv.org/abs/2304.04758)

```
@article{hu2023expectations,
  title = {{Expectations over Unspoken Alternatives Predict Pragmatic Inferences}},
  author = {Hu, Jennifer and Levy, Roger and Degen, Judith and Schuster, Sebastian},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.1162/tacl\_a\_00579},
  eprint = {2304.04758},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1162/tacl_a_00579}
}
```

- [← previous Fine-grained Information Status Classification Using Discourse Context-Aware BERT](https://md.mova.elamur.ai/free/en/docs/papers/hou-2020-fine-grained-information-status-classification/)
- [next → A fine-grained comparison of pragmatic language understanding in humans and language models](https://md.mova.elamur.ai/free/en/docs/papers/hu-2023-fine-grained-comparison-pragmatic-language/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

59 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Joris Baan, Wilker Aziz, Barbara Plank, and Raquel Fernandez. 2022. Stop measuring calibration when humans disagree. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1892–1915, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. https://aclanthology.org/2022.emnlp-main.124/ Andrea Beltrama and Ming Xiang. 2013. Is ‘good’ better than ‘excellent’? An experimental investigation on scalar implicatures and gradable adjectives. Proceedings of Sinn und Bedeutung, 17. [↗](https://aclanthology.org/2022.emnlp-main.124/)
2. Richard Breheny, Nathan Klinedinst, Jacopo Romoli, and Yasutada Sudo. 2018. The symmetry problem: Current theories and prospects. Natural Language Semantics, 26(2):85–110. https://doi.org/10.1007/s11050-017 -9141-z Brian Buccola, Manuel Križ, and Emmanuel Chemla. 2021. Conceptual alternatives: Competition in language and beyond. Linguistics and Philosophy. https://doi.org/10 .1007/s10988-021-09327-w Joan L. Bybee and Clay Beckner. 2015. Usage-based theory. In Bernd Heine and Heiko Narrog, editors, The Oxford Handbook of Linguistic Analysis. Oxford University Press. [↗](https://doi.org/10.1007/s11050-017) [our card](https://md.mova.elamur.ai/free/en/docs/papers/degen-2015-investigating-distribution-some-but-not/)
3. Judith Degen and Michael K. Tanenhaus. 2015. Processing scalar implicature: A constraint-based approach. Cognitive Science, 39(4):667–710. https://doi.org/10.1111/cogs.12171, PubMed: 25265993 [↗](https://doi.org/10.1111/cogs.12171)
4. Judith Degen and Michael K. Tanenhaus. 2016. Availability of alternatives and the processing of scalar implicatures: A visual world eye-tracking study. Cognitive Science, 40(1):172–201. https://doi.org/10.1111/cogs.12227, PubMed: 25807866 [↗](https://doi.org/10.1111/cogs.12227)
5. Richard Futrell, Ethan Wilcox, Takashi Morita, Peng Qian, Miguel Ballesteros, and Roger Levy. 2019. Neural language models as psycholinguistic subjects: Representations of syntactic state. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 32–42, Minneapolis, Minnesota. Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1004 [↗](https://doi.org/10.18653/v1/n19-1004)
6. Judith Degen, Michael Henry Tessler, and Noah D. Goodman. 2015. Wonky worlds: Listeners revise world knowledge when utterances are odd. In Proceedings of the 37th Annual Meeting of the Cognitive Science Society. [find](https://scholar.google.com/scholar?q=Judith+Degen%2C+Michael+Henry+Tessler%2C+and+Noah+D%2E+Goodman%2E+2015%2E+Wonky+worlds%3A+Listeners+revise+world+knowledge+when+utterances+are+odd%2E+In+Proceedings+of+the+37th+Annual+Meeting+of+the+Cognitive+Scien)
7. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics. https://doi.org/10 .18653/v1/N19-1423 [↗](https://aclanthology.org/N19-1423/)
8. Aina Garı́ Soler and Marianna Apidianaki. 2020. BERT knows Punta Cana is not just beautiful, it’s gorgeous: Ranking scalar adjectives with contextualised representations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7371–7385, Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.598 [↗](https://aclanthology.org/2020.emnlp-main.598/)
9. Aina Garı́ Soler and Marianna Apidianaki. 2021. Scalar adjective identification and multilingual ranking. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4653–4660, Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.370 [↗](https://aclanthology.org/2021.naacl-main.370/)
10. Ryan Doran, Rachel E. Baker, Yaron McNabb, Meredith Larson, and Gregory Ward. 2009. On the non-unified nature of scalar implicature: An empirical investigation. International Review of Pragmatics, 1(2):211–248. https://doi.org/10.1163/187730909X12538045489854 [↗](https://doi.org/10.1163/187730909x12538045489854)
11. Sarah F. V. Eiteljoerge, Nausicaa Pouscoulous, and Elena V. M. Lieven. 2018. Some pieces are missing: Implicature production in Children. Frontiers in Psychology, 9:1928. https://doi.org/10.3389/fpsyg.2018 .01928, PubMed: 30405468 [↗](https://doi.org/10.3389/fpsyg.2018)
12. Jon Gauthier, Jennifer Hu, Ethan Wilcox, Peng Qian, and Roger Levy. 2020. SyntaxGym: An online platform for targeted evaluation of language models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 70–76, Online. Association for Computational Linguistics. https://doi.org/10 .18653/v1/2020.acl-demos.10 [↗](https://aclanthology.org/2020.acl-demos.10/)
13. Danny Fox and Roni Katzir. 2011. On the characterization of alternatives. Natural Language Semantics, 19(1):87–107. https://doi.org/10.1007/s11050-010-9065-3 [↗](https://doi.org/10.1007/s11050-010-9065-3)
14. Michael C. Frank and Noah D. Goodman. 2012. Predicting pragmatic reasoning in language games. Science, 336(6084):998–998. https://doi.org/10.1126/science .1218633, PubMed: 22628647 [↗](https://doi.org/10.1126/science)
15. Gerald Gazdar. 1979. Pragmatics: Implicature, Presupposition, and Logical Form. Academic Press, New York. [find](https://scholar.google.com/scholar?q=Gerald+Gazdar%2E+1979%2E+Pragmatics%3A+Implicature%2C+Presupposition%2C+and+Logical+Form%2E+Academic+Press%2C+New+York%2E)
16. J. J. Godfrey, E. C. Holliman, and J. McDaniel. 1992. Switchboard: A telephone speech corpus for research and development. In International Conferenceon Acoustics, Speech and Signal Processing, pages 517–520. https://doi.org/10.1109/ICASSP.1992.225858 [↗](https://doi.org/10.1109/icassp.1992.225858)
17. Stefan L. Frank and Rens Bod. 2011. Insensitivity of the human sentence-processing system to hierarchical structure. Psychological Science, 22(6):829–834. https://doi.org/10.1177/0956797611409589, PubMed: 21586764 [↗](https://doi.org/10.1177/0956797611409589)
18. Noah D. Goodman and Michael C. Frank. 2016. Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences, 20(11):818–829. https://doi.org/10.1016/j.tics.2016.08.005, PubMed: 27692852 [↗](https://doi.org/10.1016/j.tics.2016.08.005)
19. Nan-Jiang Jiang and Marie-Catherine de Marneffe. 2022. Investigating reasons for disagreement in natural language inference. Transactions of the Association for Computational Linguistics, 10:1357–1374. https:// doi.org/10.1162/tacl a 00523 [↗](https://doi.org/10.1162/tacl)
20. Noah D. Goodman and Daniel Lassiter. 2015. Probabilistic semantics and pragmatics: Uncertainty in language and thought. In The Handbook of Contemporary Semantic Theory, pages 655–686. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118882139.ch21 [↗](https://doi.org/10.1002/9781118882139.ch21)
21. Roni Katzir. 2007. Structurally-defined alternatives. Linguistics and Philosophy, 30(6):669–690. https://doi.org/10.1007/s10988-008 -9029-y Joo-Kyung Kim and Marie-Catherine de Marneffe. 2013. Deriving adjectival scales from continuous space word representations. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1625–1630. Seattle, Washington, USA. Association for Computational Linguistics. https://aclanthology.org/D13 -1169 [↗](https://doi.org/10.1007/s10988-008)
22. Nicole Gotzner, Stephanie Solt, and Anton Benz. 2018. Scalar diversity, negative strengthening, and adjectival semantics. Frontiers in Psychology, 9:1659. https://doi.org/10.3389/fpsyg.2018.01659 [↗](https://doi.org/10.3389/fpsyg.2018.01659)
23. Herbert P. Grice. 1975. Logic and conversation. In Peter Cole and Jerry L. Morgan, editors, Syntax and Semantics: Speech Acts, 3, pages 41–58. Academic Press. https://doi.org/10.1163/9789004368811_003 [↗](https://doi.org/10.1163/9789004368811_003)
24. Anthony Kroch. 1972. Lexical and inferred meanings for some time adverbs. Quarterly Progress Reports of the Research Laboratory of Electronics, 104:260–267. [find](https://scholar.google.com/scholar?q=Anthony+Kroch%2E+1972%2E+Lexical+and+inferred+meanings+for+some+time+adverbs%2E+Quarterly+Progress+Reports+of+the+Research+Laboratory+of+Electronics%2C+104%3A260%E2%80%93267%2E)
25. Daniel Lassiter. 2022. How not to identify a scalar implicature (The importance of priors). Presentation at Cognitive Semantic and Quantities Workshop, University of Amsterdam. [find](https://scholar.google.com/scholar?q=Daniel+Lassiter%2E+2022%2E+How+not+to+identify+a+scalar+implicature+%28The+importance+of+priors%29%2E+Presentation+at+Cognitive+Semantic+and+Quantities+Workshop%2C+University+of+Amsterdam%2E)
26. Marti A. Hearst. 1992. Automatic acquisition of hyponyms from large text corpora. In COLING 1992 Volume 2: The 14th International Conference on Computational Linguistics. https://doi.org/10.3115/992133 .992154 [↗](https://doi.org/10.3115/992133)
27. Stephen Levinson. 2000. Presumptive Meaning: The Theory of Generalized Conversational Implicature, MIT Press. https://doi.org/10.7551/mitpress/5526.001.0001 [↗](https://doi.org/10.7551/mitpress/5526.001.0001)
28. Laurence R. Horn. 1989. A Natural History of Negation. Chicago University Press. [find](https://scholar.google.com/scholar?q=Laurence+R%2E+Horn%2E+1989%2E+A+Natural+History+of+Negation%2E+Chicago+University+Press%2E)
29. Elissa Li, Sebastian Schuster, and Judith Degen. 2021. Predicting scalar inferences from ‘‘or’’ to ‘‘not both’’ using neural sentence encoders. In Proceedings of the Society for Computation in Linguistics, volume 4. https:// doi.org/10.7275/xr01-a852 [↗](https://doi.org/10.7275/xr01-a852) [our card](https://md.mova.elamur.ai/free/en/docs/papers/li-2021-predicting-scalar-inferences-or-not/)
30. Jennifer Hu, Jon Gauthier, Peng Qian, Ethan Wilcox, and Roger Levy. 2020. A systematic assessment of syntactic generalization in neural language models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1725–1744, Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.158 [↗](https://aclanthology.org/2020.acl-main.158/)
31. Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016. Assessing the ability of LSTMs to learn syntax-sensitive dependencies. Transactions of the Association for Computational Linguistics, 4:521–535. https://doi.org/10.1162/tacl_a_00115 [↗](https://doi.org/10.1162/tacl_a_00115)
32. Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, and Adina Williams. 2020. Are natural language inference models IMPPRESsive? Learning IM-Plicature and PRESupposition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8690–8705. Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.768 [↗](https://doi.org/10.18653/v1/2020.acl-main.768) [our card](https://md.mova.elamur.ai/free/en/docs/papers/jeretic-2020-are-natural-language-inference-models/)
33. Pierre Lison and Jörg Tiedemann. 2016. Open-Subtitles2016: Extracting large parallel corpora from movie and TV subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation. https:// aclanthology.org/L16-1147/ Edward Loper and Steven Bird. 2002. NLTK: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pages 63–70, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics. https://doi.org/10.3115/1118108 .1118117 Transactions of the Association for Computational Linguistics, 7:677–694. https://doi .org/10.1162/tacl_a_00293 [↗](https://doi.org/10.3115/1118108)
34. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. Doha, Qatar. Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1162 [↗](https://doi.org/10.3115/v1/d14-1162)
35. Marie-Catherine de Marneffe, Christopher D. Manning, and Christopher Potts. 2010. ‘‘Was it good? It was provocative.’’ Learning the meaning of scalar adjectives. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 167–176, Uppsala, Sweden. Association for Computational Linguistics. https://aclanthology.org/P10-1018/ Ciyang Qing, Noah D. Goodman, and Daniel Lassiter. 2016. A rational speech-act model of projective content. In Proceedings of the 38th Annual Meeting of the Cognitive Science Society. [↗](https://aclanthology.org/P10-1018/) [our card](https://md.mova.elamur.ai/free/en/docs/papers/marneffe-2010-was-it-good-it-was/)
36. Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. [https://d4mucfpksywv.cloudfront.net/better-language-models/language](https://d4mucfpksywv.cloudfront.net/better-language-models/language) models are unsupervised multitask learners.pdf David Marr. 1982. Vision: A Computational Approach. Freeman & Co., San Francisco. [↗](https://d4mucfpksywv.cloudfront.net/better-language-models/language)
37. Gerard de Melo and Mohit Bansal. 2013. Good, great, excellent: Global inference of semantic intensities. Transactions of the Association for Computational Linguistics, 1:279–290. https://doi.org/10.1162/tacl_a_00227 [↗](https://doi.org/10.1162/tacl_a_00227)
38. Craige Roberts. 2012. Information structure in discourse: Towards an integrated formal theory of pragmatics. Semantics and Pragmatics, 5(6):1–69. https://doi.org/10 .3765/sp.5.6 [↗](https://doi.org/10.3765/sp.5.6) [our card](https://md.mova.elamur.ai/free/en/docs/papers/roberts-2012-information-structure-discourse-towards-integrated/)
39. Danny Merkx and Stefan L. Frank. 2021. Human sentence processing: Recurrence or attention? In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 12–22. Online. Association for Computational Linguistics. https://doi.org/10 .18653/v1/2021.cmcl-1.2 [↗](https://aclanthology.org/2021.cmcl-1.2/)
40. Eszter Ronai and Ming Xiang. 2021. Exploring the connection between question under discussion and scalar diversity. In Proceedings of the Linguistic Society of America, volume 6, pages 649–662. https://doi.org/10.3765/plsa.v6i1.5001 [↗](https://doi.org/10.3765/plsa.v6i1.5001)
41. G. A. Miller. 1995. WordNet: A lexical database for English. Communications of the ACM, 38(11):39–41. https://doi.org/10.1145/219717.219748 [↗](https://doi.org/10.1145/219717.219748) [our card](https://md.mova.elamur.ai/free/en/docs/papers/lex-1994-miller-wordnet/)
42. Eszter Ronai and Ming Xiang. 2022. Three factors in explaining scalar diversity. In Proceedings of Sinn und Bedeutung 26. [↗](https://doi.org/10.18148/sub/2022.v26i0.1026)
43. Emiel van Miltenburg. 2015. Detecting and ordering adjectival scalemates. In Proceedings of MAPLEX, Yamagata, Japan. https:// arxiv.org/abs/1504.08102 [↗](https://arxiv.org/abs/1504.08102)
44. Mats E. Rooth. 1985. Association with Focus. PhD thesis, University of Massachusetts. [find](https://scholar.google.com/scholar?q=Mats+E%2E+Rooth%2E+1985%2E+Association+with+Focus%2E+PhD+thesis%2C+University+of+Massachusetts%2E)
45. Sebastian Schuster, Yuxing Chen, and Judith Degen. 2020. Harnessing the linguistic signal to predict scalar inferences. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5387–5403, Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.479 [↗](https://aclanthology.org/2020.acl-main.479/) [our card](https://md.mova.elamur.ai/free/en/docs/papers/schuster-2020-harnessing-linguistic-signal-predict-scalar/)
46. Elizabeth Pankratz and Bob van Tiel. 2021. The role of relevance for scalar diversity: A usage-based approach. Language and Cognition, 13(4):562–594. https://doi.org/10.1017/langcog.2021.13 [↗](https://doi.org/10.1017/langcog.2021.13)
47. Ellie Pavlick and Tom Kwiatkowski. 2019. Inherent disagreements in human textual inferences. [↗](https://doi.org/10.1162/tacl_a_00293) [our card](https://md.mova.elamur.ai/free/en/docs/papers/pavlick-2019-inherent-disagreements-human-textual-inferences/)
48. Chaitanya Shivade, Marie-Catherine de Marneffe, Eric Fosler-Lussier, and Albert M. Lai. 2015. Corpus-based discovery of semantic intensity scales. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 483–493. Denver, Colorado. Association for Computational Linguistics. https://doi.org/10.3115/v1/N15-1051 [↗](https://aclanthology.org/N15-1051/)
49. Albert Webson, Alyssa Marie Loo, Qinan Yu, and Ellie Pavlick. 2023. Are language models worse than humans at following prompts? It’s complicated. https://doi.org/10.48550/arXiv.2301.07085 [↗](https://doi.org/10.18653/v1/2023.findings-emnlp.514)
50. Matthijs Westera and Gemma Boleda. 2020. A closer look at scalar diversity using contextualized semantic similarity. Proceedings of Sinn und Bedeutung, 24(2):439–454. [↗](https://doi.org/10.18148/sub/2020.v24i2.908)
51. Ethan Wilcox, Jon Gauthier, Jennifer Hu, Peng Qian, and Roger Levy. 2020. On the predictive power of neural language models for human real-time comprehension behavior. In Proceedings of the 42nd Annual Meeting of the Cognitive Science Society. https:// arxiv.org/abs/2006.01912 [↗](https://arxiv.org/abs/2006.01912)
52. Les Sikos, Noortje J. Venhuizen, Heiner Drenhaus, and Matthew W. Crocker. 2021. Reevaluating pragmatic reasoning in language games. PLOS ONE, 16(3):e0248388. https:// doi.org/10.1371/journal.pone.0248388, PubMed: 33730097 [↗](https://doi.org/10.1371/journal.pone.0248388)
53. Nathaniel J. Smith and Roger Levy. 2013. The effect of word predictability on reading time is logarithmic. Cognition, 128(3):302–319. https://doi.org/10.1016/j.cognition .2013.02.013, PubMed: 23747651 [↗](https://doi.org/10.1016/j.cognition)
54. Dan Sperber and Deirdre Wilson. 1986. Relevance: Communication and Cognition. Wiley-Blackwell. [find](https://scholar.google.com/scholar?q=Dan+Sperber+and+Deirdre+Wilson%2E+1986%2E+Relevance%3A+Communication+and+Cognition%2E+Wiley%2DBlackwell%2E)
55. Chao Sun, Ye Tian, and Richard Breheny. 2018. A link between local enrichment and scalar diversity. Frontiers in Psychology, 9:2092. https://doi.org/10.3389/fpsyg.2018 .02092, PubMed: 30443233 [↗](https://doi.org/10.3389/fpsyg.2018)
56. Bob van Tiel, Emiel van Miltenburg, Natalia Zevakhina, and Bart Geurts. 2016. Scalar diversity. Journal of Semantics, 33(1):137–175. https://doi.org/10.1093/jos/ffu017 [↗](https://doi.org/10.1093/jos/ffu017)
57. Michael Tomasello. 2003. Constructing A Language: A Usage-based Theory of Language Acquisition, Harvard University Press. [find](https://scholar.google.com/scholar?q=Michael+Tomasello%2E+2003%2E+Constructing+A+Language%3A+A+Usage%2Dbased+Theory+of+Language+Acquisition%2C+Harvard+University+Press%2E)
58. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics. https://doi.org/10 .18653/v1/2020.emnlp-demos.6 [↗](https://aclanthology.org/2020.emnlp-demos.6/)
59. Zheng Zhang, Leon Bergen, Alexander Paunov, Rachel Ryskin, and Edward Gibson. 2023. Scalar implicature is sensitive to contextual alternatives. Cognitive Science, 47(2). https://doi.org/10.1111/cogs.13238, PubMed: 36739521 [↗](https://doi.org/10.1111/cogs.13238)