Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision

Sandro Pezzelle, Ionut-Teodor Sorodoc, Raffaella Bernardi


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.
Anthology ID:
N18-1039
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
419–430
Language:
URL:
https://aclanthology.org/N18-1039
DOI:
10.18653/v1/N18-1039
Bibkey:
Cite (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.
Cite (Informal):
Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision (Pezzelle et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1039.pdf
Video:
 http://vimeo.com/277631187
Code
 sandropezzelle/multitask-quant