7 citations · 9 across the 5 of their papers we have counts for
6 papers
Evaluating Online Continual Learning with CALM
Germán Kruszewski, Ionut-Teodor Sorodoc, Tomas Mikolov
Online Continual Learning (OCL) studies learning over a continuous data stream without observing any single example more than once, a setting that is closer to the experience of hu…
Recurrent Instance Segmentation using Sequences of Referring Expressions
Alba Herrera-Palacio, Carles Ventura, Carina Silberer +3
The goal of this work is to segment the objects in an image that are referred to by a sequence of linguistic descriptions (referring expressions). We propose a deep neural network…
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue
Laura Aina, Carina Silberer, Matthijs Westera +2
Humans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations ha…
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library
Laura Aina, Carina Silberer, Ionut-Teodor Sorodoc +2
This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. It is a simple, standard model with one key innovation, an e…
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…
Pay Attention to Those Sets! Learning Quantification from Images
Ionut Sorodoc, Sandro Pezzelle, Aurélie Herbelot +2
Major advances have recently been made in merging language and vision representations. But most tasks considered so far have confined themselves to the processing of objects and le…