DeepAI AI Chat
Log In Sign Up

Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off

by   Yuchen Lian, et al.

Artificial learners often behave differently from human learners in the context of neural agent-based simulations of language emergence and change. The lack of appropriate cognitive biases in these learners is one of the prevailing explanations. However, it has also been proposed that more naturalistic settings of language learning and use could lead to more human-like results. In this work, we investigate the latter account focusing on the word-order/case-marking trade-off, a widely attested language universal which has proven particularly difficult to simulate. We propose a new Neural-agent Language Learning and Communication framework (NeLLCom) where pairs of speaking and listening agents first learn a given miniature language through supervised learning, and then optimize it for communication via reinforcement learning. Following closely the setup of earlier human experiments, we succeed in replicating the trade-off with the new framework without hard-coding any learning bias in the agents. We see this as an essential step towards the investigation of language universals with neural learners.


page 8

page 10


Multi-lingual agents through multi-headed neural networks

This paper considers cooperative Multi-Agent Reinforcement Learning, foc...

Enhancing Agent Communication and Learning through Action and Language

We introduce a novel category of GC-agents capable of functioning as bot...

The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning

Natural languages commonly display a trade-off among different strategie...

The Emergence of the Shape Bias Results from Communicative Efficiency

By the age of two, children tend to assume that new word categories are ...

Spatial community structure impedes language amalgamation in a population-based iterated learning model

The iterated learning model is an agent-based model of language evolutio...

Towards Multi-Agent Communication-Based Language Learning

We propose an interactive multimodal framework for language learning. In...

Incentivizing the Emergence of Grounded Discrete Communication Between General Agents

We converted the recently developed BabyAI grid world platform to a send...