@InProceedings{pezzelle-sorodoc-bernardi:2018:N18-1,
  author    = {Pezzelle, Sandro  and  Sorodoc, Ionut-Teodor  and  Bernardi, Raffaella},
  title     = {Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision},
  booktitle = {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},
  publisher = {Association for Computational Linguistics},
  pages     = {419--430},
  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.},
  url       = {http://www.aclweb.org/anthology/N18-1039}
}