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Found 7 papers, 5 papers with code
Controlled tasks for model analysis: Retrieving discrete information from sequences
In recent years, the NLP community has shown increasing interest in analysing how deep learning models work.
Probing for Referential Information in Language Models
Language models keep track of complex information about the preceding context {--} including, e. g., syntactic relations in a sentence.
Evaluating Online Continual Learning with CALM
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 humans and systems that must learn "on-the-wild".
Recurrent Instance Segmentation using Sequences of Referring Expressions
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).
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue
In this paper we analyze the behavior of two recently proposed entity-centric models in a referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4).
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library
This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues.
Comparatives, Quantifiers, Proportions: A Multi-Task Model for the Learning of Quantities from Vision
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.
