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
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/N18-1039.pdf
- Code
- sandropezzelle/multitask-quant