This week’s MCQLL meeting (Thursday, March 11th, 1:30-2:30pm), will feature a talk from Eva Portelance. Abstract and bio are below.

If you would like to join the meeting but have not yet registered for this semester’s MCQLL meetings, please send an email to

Bio: Eva is currently a Ph.D. candidate at Stanford University in Linguistics, working with Mike Frank and Dan Jurafsky. She completed a B.A. Honours in Linguistics and Computer Science at McGill University in 2017. She is interested in linguistic structure and language learning both in humans and machines. This work was started during an internship at Microsoft Research Montreal.

Abstract: Learning Strategies for the Emergence of Language in Iterated Learning

In emergent communication studies, agents play communication games in order to develop a set of linguistic conventions referred to as the emergent language. Here, we compare the effects of a variety of learning functions and play phases on the efficiency and effectiveness of emergent language learning. We do so both within a single generation of agents and across generations in an iterated learning setting. We find that allowing agents to engage in forms of selfplay ultimately leads to more effective communication. In the iterated learning setting we compare different approaches to intergenerational learning. We find that selfplay used jointly with imitation can also lead to effective communication in this setting. Additionally, we find that encouraging agents to successfully communicate with previous generations rather than to successfully imitate them can lead to both effective language and efficient learning. Finally, we introduce a new dataset and a new agent architecture with split visual perception and representation modules in order to conduct our experiments.