Compositionality and Generalization in Emergent Languages

Association for Computational Linguistics (ACL)


Natural language allows us to refer to novel composite concepts by combining expressions denoting their parts according to systematic rules, a property known as compositionality. In this paper, we study whether the language emerging in deep multi-agent simulations possesses a similar ability to refer to novel primitive combinations, and whether it accomplishes this feat by strategies akin to human-language compositionality.
Equipped with new ways to measure compositionality in emergent languages inspired by disentanglement in representation learning, we
establish three main results. First, given sufficiently large input spaces, the emergent language will naturally develop the ability to refer to novel composite concepts. Second, there is no correlation between the degree of compositionality of an emergent language and its ability to generalize. Third, while compositionality is not necessary for generalization, it provides an advantage in terms of language transmission: The more compositional a language is, the more easily it will be picked up by new learners, even when the latter differ in architecture from the original agents. We conclude that compositionality does not arise from simple generalization pressure, but if an emergent language does chance upon it, it will be more likely to survive and thrive.

Related Publications

All Publications

NAACL - June 6, 2021

Deep Learning on Graphs for Natural Language Processing

Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li

ICASSP - June 6, 2021

On the Predictability of HRTFs from Ear Shapes Using Deep Networks

Yaxuan Zhou, Hao Jiang, Vamsi Krishna Ithapu

CoRL - December 1, 2020

Auxiliary Tasks Speed Up Learning PointGoal Navigation

Joel Ye, Dhruv Batra, Erik Wijmans, Abhishek Das

ACL - July 7, 2020

CraftAssist Instruction Parsing: Semantic Parsing for a Voxel-World Assistant

Kavya Srinet, Yacine Jernite, Jonathan Gray, Arthur Szlam

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy