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Contrafactives and Learnability: An Experiment with Propositional Constants

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Logic and Engineering of Natural Language Semantics (LENLS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14213))

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Abstract

Holton has drawn attention to a new semantic universal, according to which no natural language has contrafactive attitude verbs. Because factives are universal across natural languages, Holton’s universal is part of a major asymmetry between factive and contrafactive attitude verbs. We previously proposed that this asymmetry arises partly because the meaning of contrafactives is significantly harder to learn than that of factives. Here we extend our work by describing an additional computational experiment that further supports our hypothesis.

This paper reports on research supported by Cambridge University Press and Assessment, University of Cambridge. We thank the NVIDIA Corporation for the donation of the Titan X Pascal GPU used in this research. Simon Wimmer’s work on this paper was supported by a postdoc stipend of the Fritz Thyssen Foundation. We thank audiences in Bochum, Dortmund, Essen, Tokyo, and Utrecht, and anonymous reviewers for LENLS19 and AC23 for discussion of related material. David Strohmaier designed and ran both computational experiments, Simon Wimmer brought philosophical and linguistic discussions to bear on their design and interpretation.

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Notes

  1. 1.

    Holton adopts two further conditions expressions musts satisfy to count as contrafactives. In parallel with know, he would regard contra as a mental state verb and as responsive (embedding declarative and interrogative complements). For present purposes, however, we set these conditions aside. We take the question of why no natural language has a verb with the features noted in the text to be of independent interest, and expect the work we present here to also go some way toward addressing why no natural language has a verb that satisfies all of Holton’s conditions.

  2. 2.

    Although a non-factive (e.g. believe or think) entails a belief too, it contrasts with factives and contrafactives in triggering neither an uncancelleable inference to the truth/falsity of its declarative complement nor an inference to truth/falsity that projects through entailment-cancelling environments.

  3. 3.

    Another reason is that disprove neither entails a belief that its declarative complement is true nor is a mental state verb, though Hyman explicitly questions the mental state condition, making appeal to that condition dialectically ineffective.

  4. 4.

    [12] also lists pretend and lie as counterexamples to Holton’s universal. But in [8, p. 247], Holton denies that pretend is a contrafactive as its falsity inference is cancellable and in [9], he argues that lie does not embed declarative complements.

  5. 5.

    Most likely there are further reasons for the absence of contrafactives. In future work, we will survey how the costs and benefits of contrafactives tally up.

  6. 6.

    We thank Dilara Malkoc for discussion of the Turkish data.

  7. 7.

    We did not train our network to handle ascriptions in entailment-cancelling environments. We plan to fill this gap in a follow-up experiment.

  8. 8.

    Further details and a comparison of this experiment with others in the literature can be found in [30]. A link to our paper, the code for our network, and further information are available on GitHub [29].

  9. 9.

    A list of the available as well as the best-performing hyperparameters can be found in our online appendix on GitHub [29].

  10. 10.

    Here and below we used the permutation test included in the scipy library.

  11. 11.

    The code for this network and further details are also available on GitHub [29].

  12. 12.

    We could also expand the main training set for the final evaluation, i.e. add the excluded instances of the propositional constants and corresponding representations. However, our approach tests the model’s ability to generalise more strictly, as we require it to generalise to attitude ascriptions it has not seen before.

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Authors and Affiliations

  1. Department of Computer Science and Technology, ALTA Institute, University of Cambridge, Cambridge, UK

    David Strohmaier

  2. Institut für Philosophie und Politikwissenschaft, Technische Universität Dortmund, Dortmund, Germany

    Simon Wimmer

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  1. David Strohmaier
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Correspondence to Simon Wimmer .

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Editors and Affiliations

  1. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

  2. Keio University, Tokyo, Japan

    Koji Mineshima

  3. Aoyama Gakuin University, Tokyo, Japan

    Elin McCready

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Strohmaier, D., Wimmer, S. (2023). Contrafactives and Learnability: An Experiment with Propositional Constants. In: Bekki, D., Mineshima, K., McCready, E. (eds) Logic and Engineering of Natural Language Semantics. LENLS 2022. Lecture Notes in Computer Science, vol 14213. Springer, Cham. https://doi.org/10.1007/978-3-031-43977-3_5

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  • DOI: https://doi.org/10.1007/978-3-031-43977-3_5

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