The ability to cooperate through language is a defining feature of humans. As the
perceptual, motory and planning capabilities of deep artificial networks increase …

Rapid progress in machine learning for natural language processing has the potential to
transform debates about how humans learn language. However, the learning environments …

Emergent communication aims for a better understanding of human language evolution and
building more efficient representations. We posit that reaching these goals will require …

We present a method for combining multi-agent communication and traditional data-driven
approaches to natural language learning, with an end goal of teaching agents to …

Despite renewed interest in emergent language simulations with neural networks, little is
known about the basic properties of the induced code, and how they compare to human …

In the recent shift towards human-centric AI, the need for machines to accurately use natural
language has become increasingly important. While a common approach to achieve this is …

Transformer-based deep NLP models are trained using hundreds of millions of parameters,
limiting their applicability in computationally constrained environments. In this paper, we …

This paper investigates contextual word representation models from the lens of similarity
analysis. Given a collection of trained models, we measure the similarity of their internal …

No man is an island. Humans develop the ability to communicate with a large community by
coordinating with different interlocutors within short conversations. This ability is largely …

There is renewed interest in simulating language emergence among deep neural agents
that communicate to jointly solve a task, spurred by the practical aim to develop language …