Computational Modelling of Quantifier Use: Corpus, Models, and Evaluation

Publication date

2023-05-30

Authors

Chen, GuanyiISNI 0000000492852701
Deemter, Kees vanISNI 0000000115590531

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Document Type

Article
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cc_by

Abstract

A prominent strand of work in formal semantics investigates the ways in which human languages quantify the elements of a set, as when we say All A are B, Few A are B, and so on. Building on a growing body of empirical studies that shed light on the meaning and the use of quantifiers, we extend this line of work by computationally modelling how human speakers textually describe complex scenes in which quantitative relations play an important role. To this end, we conduct a series of elicitation experiments in which human speakers were asked to perform a linguistic task that invites the use of quantified expressions. The experiments result in a corpus, called QTUNA, made up of short texts that contain a large variety of quantified expressions. We analyse QTUNA, summarise our findings, and explain how we design computational models of human quantifier use accordingly. Finally, we evaluate these models in accordance with QTUNA.

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Citation

Chen, G & van Deemter, K 2023, 'Computational Modelling of Quantifier Use: Corpus, Models, and Evaluation', Journal of Artificial Intelligence Research, vol. 77, pp. 167-206. https://doi.org/10.1613/jair.1.13899