6 research outputs found
Emergent Language Generalization and Acquisition Speed are not tied to Compositionality
Studies of discrete languages emerging when neural agents communicate to
solve a joint task often look for evidence of compositional structure. This
stems for the expectation that such a structure would allow languages to be
acquired faster by the agents and enable them to generalize better. We argue
that these beneficial properties are only loosely connected to
compositionality. In two experiments, we demonstrate that, depending on the
task, non-compositional languages might show equal, or better, generalization
performance and acquisition speed than compositional ones. Further research in
the area should be clearer about what benefits are expected from
compositionality, and how the latter would lead to them
Emergent Communication Pretraining for Few-Shot Machine Translation
While state-of-the-art models that rely upon massively multilingual
pretrained encoders achieve sample efficiency in downstream applications, they
still require abundant amounts of unlabelled text. Nevertheless, most of the
world's languages lack such resources. Hence, we investigate a more radical
form of unsupervised knowledge transfer in the absence of linguistic data. In
particular, for the first time we pretrain neural networks via emergent
communication from referential games. Our key assumption is that grounding
communication on images---as a crude approximation of real-world
environments---inductively biases the model towards learning natural languages.
On the one hand, we show that this substantially benefits machine translation
in few-shot settings. On the other hand, this also provides an extrinsic
evaluation protocol to probe the properties of emergent languages ex vitro.
Intuitively, the closer they are to natural languages, the higher the gains
from pretraining on them should be. For instance, in this work we measure the
influence of communication success and maximum sequence length on downstream
performances. Finally, we introduce a customised adapter layer and annealing
strategies for the regulariser of maximum-a-posteriori inference during
fine-tuning. These turn out to be crucial to facilitate knowledge transfer and
prevent catastrophic forgetting. Compared to a recurrent baseline, our method
yields gains of in BLEU score with only NMT
training instances and with NMT training
instances across four language pairs. These proof-of-concept results reveal the
potential of emergent communication pretraining for both natural language
processing tasks in resource-poor settings and extrinsic evaluation of
artificial languages
Explainability in Deep Reinforcement Learning
A large set of the explainable Artificial Intelligence (XAI) literature is
emerging on feature relevance techniques to explain a deep neural network (DNN)
output or explaining models that ingest image source data. However, assessing
how XAI techniques can help understand models beyond classification tasks, e.g.
for reinforcement learning (RL), has not been extensively studied. We review
recent works in the direction to attain Explainable Reinforcement Learning
(XRL), a relatively new subfield of Explainable Artificial Intelligence,
intended to be used in general public applications, with diverse audiences,
requiring ethical, responsible and trustable algorithms. In critical situations
where it is essential to justify and explain the agent's behaviour, better
explainability and interpretability of RL models could help gain scientific
insight on the inner workings of what is still considered a black box. We
evaluate mainly studies directly linking explainability to RL, and split these
into two categories according to the way the explanations are generated:
transparent algorithms and post-hoc explainaility. We also review the most
prominent XAI works from the lenses of how they could potentially enlighten the
further deployment of the latest advances in RL, in the demanding present and
future of everyday problems.Comment: Article accepted at Knowledge-Based System
Explainability in Deep Reinforcement Learning
International audienceA large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining models that ingest image source data. However, assessing how XAI techniques can help understand models beyond classification tasks, e.g. for reinforcement learning (RL), has not been extensively studied. We review recent works in the direction to attain Explainable Reinforcement Learning (XRL), a relatively new subfield of Explainable Artificial Intelligence, intended to be used in general public applications, with diverse audiences, requiring ethical, responsible and trustable algorithms. In critical situations where it is essential to justify and explain the agent's behaviour, better explainability and interpretability of RL models could help gain scientific insight on the inner workings of what is still considered a black box. We evaluate mainly studies directly linking explainability to RL, and split these into two categories according to the way the explanations are generated: transparent algorithms and post-hoc explainaility. We also review the most prominent XAI works from the lenses of how they could potentially enlighten the further deployment of the latest advances in RL, in the demanding present and future of everyday problems