Search Results for author: Elia Bruni

Found 29 papers, 9 papers with code

Emergence of hierarchical reference systems in multi-agent communication

1 code implementation24 Mar 2022 Xenia Ohmer, Marko Duda, Elia Bruni

We develop a novel communication game, the hierarchical reference game, to study the emergence of such reference systems in artificial agents.

Novel Concepts

Language Modelling as a Multi-Task Problem

no code implementations EACL 2021 Lucas Weber, Jaap Jumelet, Elia Bruni, Dieuwke Hupkes

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.

Language Modelling Multi-Task Learning

The Grammar of Emergent Languages

1 code implementation EMNLP 2020 Oskar van der Wal, Silvan de Boer, Elia Bruni, Dieuwke Hupkes

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

Compositional properties of emergent languages in deep learning

no code implementations23 Jan 2020 Bence Keresztury, Elia Bruni

Recent findings in multi-agent deep learning systems point towards the emergence of compositional languages.

Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning

no code implementations13 Jan 2020 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.

Classification General Classification +1

Co-evolution of language and agents in referential games

1 code implementation EACL 2021 Gautier Dagan, Dieuwke Hupkes, Elia Bruni

However, they do not take into account a second constraint considered to be fundamental for the shape of human language: that it must be learnable by new language learners.

Generalizing Emergent Communication

no code implementations6 Jan 2020 Thomas A. Unger, Elia Bruni

We converted the recently developed BabyAI grid world platform to a sender/receiver setup in order to test the hypothesis that established deep reinforcement learning techniques are sufficient to incentivize the emergence of a grounded discrete communication protocol between generalized agents.

reinforcement-learning Transfer Learning

Learning to Request Guidance in Emergent Communication

no code implementations11 Dec 2019 Benjamin Kolb, Leon Lang, Henning Bartsch, Arwin Gansekoele, Raymond Koopmanschap, Leonardo Romor, David Speck, Mathijs Mul, Elia Bruni

Previous research into agent communication has shown that a pre-trained guide can speed up the learning process of an imitation learning agent.

Imitation Learning

Location Attention for Extrapolation to Longer Sequences

no code implementations ACL 2020 Yann Dubois, Gautier Dagan, Dieuwke Hupkes, Elia Bruni

We hypothesize that models with a separate content- and location-based attention are more likely to extrapolate than those with common attention mechanisms.

Learning to request guidance in emergent language

no code implementations WS 2019 Benjamin Kolb, Leon Lang, Henning Bartsch, Arwin Gansekoele, Raymond Koopmanschap, Leonardo Romor, David Speck, Mathijs Mul, Elia Bruni

Previous research into agent communication has shown that a pre-trained guide can speed up the learning process of an imitation learning agent.

Imitation Learning

Compositionality decomposed: how do neural networks generalise?

no code implementations22 Aug 2019 Dieuwke Hupkes, Verna Dankers, Mathijs Mul, Elia Bruni

Despite a multitude of empirical studies, little consensus exists on whether neural networks are able to generalise compositionally, a controversy that, in part, stems from a lack of agreement about what it means for a neural model to be compositional.

Mastering emergent language: learning to guide in simulated navigation

no code implementations14 Aug 2019 Mathijs Mul, Diane Bouchacourt, Elia Bruni

A typical setup to achieve this is with a scripted teacher which guides a virtual agent using language instructions.


Assessing incrementality in sequence-to-sequence models

1 code implementation WS 2019 Dennis Ulmer, Dieuwke Hupkes, Elia Bruni

Since their inception, encoder-decoder models have successfully been applied to a wide array of problems in computational linguistics.

The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue

no code implementations ACL 2019 Janosch Haber, Tim Baumgärtner, Ece Takmaz, Lieke Gelderloos, Elia Bruni, Raquel Fernández

This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation.

Transcoding compositionally: using attention to find more generalizable solutions

1 code implementation WS 2019 Kris Korrel, Dieuwke Hupkes, Verna Dankers, Elia Bruni

While sequence-to-sequence models have shown remarkable generalization power across several natural language tasks, their construct of solutions are argued to be less compositional than human-like generalization.

On the Realization of Compositionality in Neural Networks

no code implementations WS 2019 Joris Baan, Jana Leible, Mitja Nikolaus, David Rau, Dennis Ulmer, Tim Baumgärtner, Dieuwke Hupkes, Elia Bruni

We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task.

Learning compositionally through attentive guidance

no code implementations20 May 2018 Dieuwke Hupkes, Anand Singh, Kris Korrel, German Kruszewski, Elia Bruni

While neural network models have been successfully applied to domains that require substantial generalisation skills, recent studies have implied that they struggle when solving the task they are trained on requires inferring its underlying compositional structure.

Ask No More: Deciding when to guess in referential visual dialogue

1 code implementation COLING 2018 Ravi Shekhar, Tim Baumgartner, Aashish Venkatesh, Elia Bruni, Raffaella Bernardi, Raquel Fernandez

We make initial steps towards this general goal by augmenting a task-oriented visual dialogue model with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess.

Decision Making Visual Dialog

Adversarial evaluation for open-domain dialogue generation

no code implementations WS 2017 Elia Bruni, Raquel Fern{\'a}ndez

We investigate the potential of adversarial evaluation methods for open-domain dialogue generation systems, comparing the performance of a discriminative agent to that of humans on the same task.

Dialogue Generation

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