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        <title>Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision</title>
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    <abstract>The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.</abstract>
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%0 Conference Proceedings
%T Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision
%A Pezzelle, Sandro
%A Sorodoc, Ionut-Teodor
%A Bernardi, Raffaella
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F pezzelle-etal-2018-comparatives
%X The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.
%R 10.18653/v1/N18-1039
%U https://aclanthology.org/N18-1039
%U https://doi.org/10.18653/v1/N18-1039
%P 419-430

Markdown (Informal)

[Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision](https://aclanthology.org/N18-1039) (Pezzelle et al., NAACL 2018)

• Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision (Pezzelle et al., NAACL 2018)

ACL

• Sandro Pezzelle, Ionut-Teodor Sorodoc, and Raffaella Bernardi. 2018. Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 419–430, New Orleans, Louisiana. Association for Computational Linguistics.