5 papers
Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers
Sandro Pezzelle, Shane Steinert-Threlkeld, Raffaela Bernardi +1
We study the role of linguistic context in predicting quantifiers (`few', `all'). We collect crowdsourced data from human participants and test various models in a local (single-se…
Comparatives, Quantifiers, Proportions: A Multi-Task Model for the Learning of Quantities from Vision
Sandro Pezzelle, Ionut-Teodor Sorodoc, Raffaella Bernardi
The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual sce…
FOIL it! Find One mismatch between Image and Language caption
Ravi Shekhar, Sandro Pezzelle, Yauhen Klimovich +4
In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension o…
Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision
Sandro Pezzelle, Marco Marelli, Raffaella Bernardi
People can refer to quantities in a visual scene by using either exact cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few, most, all). In humans, these two…
The LAMBADA dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou +6
We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative…