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08/23/2023
Curriculum Learning with Adam: The Devil Is in the Wrong Details
Curriculum learning (CL) posits that machine learning models – similar t...
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05/19/2023
Evaluating task understanding through multilingual consistency: A ChatGPT case study
At the staggering pace with which the capabilities of large language mod...
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03/24/2022
Emergence of hierarchical reference systems in multi-agent communication
In natural language, referencing objects at different levels of specific...
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08/12/2021
The paradox of the compositionality of natural language: a neural machine translation case study
Moving towards human-like linguistic performance is often argued to requ...
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01/27/2021
Language Modelling as a Multi-Task Problem
In this paper, we propose to study language modelling as a multi-task pr...
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10/05/2020
The Grammar of Emergent Languages
In this paper, we consider the syntactic properties of languages emerged...
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04/08/2020
Internal and external pressures on language emergence: least effort, object constancy and frequency
In previous work, artificial agents were shown to achieve almost perfect...
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01/23/2020
Compositional properties of emergent languages in deep learning
Recent findings in multi-agent deep learning systems point towards the e...
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01/13/2020
Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning
In this work, we present an alternative approach to making an agent comp...
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01/10/2020
Co-evolution of language and agents in referential games
Referential games offer a grounded learning environment for neural agent...
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01/06/2020
Incentivizing the Emergence of Grounded Discrete Communication Between General Agents
We converted the recently developed BabyAI grid world platform to a send...
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12/11/2019
Learning to Request Guidance in Emergent Communication
Previous research into agent communication has shown that a pre-trained ...
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11/10/2019
Location Attention for Extrapolation to Longer Sequences
Neural networks are surprisingly good at interpolating and perform remar...
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08/22/2019
The compositionality of neural networks: integrating symbolism and connectionism
Despite a multitude of empirical studies, little consensus exists on whe...
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08/14/2019
Mastering emergent language: learning to guide in simulated navigation
To cooperate with humans effectively, virtual agents need to be able to ...
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06/07/2019
Assessing incrementality in sequence-to-sequence models
Since their inception, encoder-decoder models have successfully been app...
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06/04/2019
The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue
This paper introduces the PhotoBook dataset, a large-scale collection of...
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06/04/2019
On the Realization of Compositionality in Neural Networks
We present a detailed comparison of two types of sequence to sequence mo...
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06/04/2019
Transcoding compositionally: using attention to find more generalizable solutions
While sequence-to-sequence models have shown remarkable generalization p...
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09/17/2018
The Fast and the Flexible: training neural networks to learn to follow instructions from small data
Learning to follow human instructions is a challenging task because whil...
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09/10/2018
Jointly Learning to See, Ask, and GuessWhat
We are interested in understanding how the ability to ground language in...
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05/20/2018
Learning compositionally through attentive guidance
In this paper, we introduce Attentive Guidance (AG), a new mechanism to ...
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05/17/2018
Ask No More:Deciding when to guess in referential visual dialogue
Our goal is to explore how the abilities brought in by a dialogue manage...
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03/01/2016