activity
20182021
most citedCompositional properties of emergent languages in deep learning

4 citations · 6 across the 5 of their papers we have counts for

collaborators

19 papers

cs.CL2021

Language Modelling as a Multi-Task Problem

Lucas Weber, Jaap Jumelet, Elia Bruni +1

In this paper, we propose to study language modelling as a multi-task problem, bringing together three strands of research: multi-task learning, linguistics, and interpretability.…

cs.CL2020

The Grammar of Emergent Languages

Oskar van der Wal, Silvan de Boer, Elia Bruni +1

In this paper, we consider the syntactic properties of languages emerged in referential games, using unsupervised grammar induction (UGI) techniques originally designed to analyse…

cs.CL2020

Internal and external pressures on language emergence: least effort, object constancy and frequency

Diana Rodríguez Luna, Edoardo Maria Ponti, Dieuwke Hupkes +1

In previous work, artificial agents were shown to achieve almost perfect accuracy in referential games where they have to communicate to identify images. Nevertheless, the resultin…

cs.LG20204 cited

Compositional properties of emergent languages in deep learning

Bence Keresztury, Elia Bruni

Recent findings in multi-agent deep learning systems point towards the emergence of compositional languages. These claims are often made without exact analysis or testing of the la…

cs.AI20201 cited

Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning

Michiel van der Meer, Matteo Pirotta, Elia Bruni

In this work, we present an alternative approach to making an agent compositional through the use of a diagnostic classifier. Because of the need for explainable agents in automate…

cs.CL2020

Co-evolution of language and agents in referential games

Gautier Dagan, Dieuwke Hupkes, Elia Bruni

Referential games offer a grounded learning environment for neural agents which accounts for the fact that language is functionally used to communicate. However, they do not take i…