2026-04-13T06:50:55Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/327032025-12-20T16:56:06Zcom_2072_6col_2072_452952
RECERCAT
author
Boleda, Gemma
author
Sorodoc, Ionut-Teodor
author
Lazaridou, Angeliki
author
Herbelot, Aurélie
author
Pezzelle, Sandro
author
Bernardi, Raffaella
2017-08-25T17:17:10Z2017-08-25T17:17:10Z2016
In this paper, we investigate whether a neural network model can learn the meaning of natural language quantifiers (no,some and all) from their use in visual contexts. We show that memory networks perform
well in this task, and that explicit counting is not necessary to the system’s performance, supporting psycholinguistic evidence on the acquisition of quantifiers.This project has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 655577 (LOVe); ERC 2011 Starting Independent Research Grant n. 283554 (COMPOSES).
© ACL, Creative Commons Attribution 4.0 License http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess
Language and vision
Grounding
Quantification
Distributed representations
Semantics
Computational semantics
Computational Linguistics
Natural Language Processing
“Look, some green circles!”: learning to quantify from images
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion