EGG: a toolkit for research on Emergence of lanGuage in Games

Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni


Abstract
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-enabled interactive AIs, as well as by theoretical questions about the evolution of human language. However, optimizing deep architectures connected by a discrete communication channel (such as that in which language emerges) is technically challenging. We introduce EGG, a toolkit that greatly simplifies the implementation of emergent-language communication games. EGG’s modular design provides a set of building blocks that the user can combine to create new games, easily navigating the optimization and architecture space. We hope that the tool will lower the technical barrier, and encourage researchers from various backgrounds to do original work in this exciting area.
Anthology ID:
D19-3010
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
55–60
Language:
URL:
https://aclanthology.org/D19-3010
DOI:
10.18653/v1/D19-3010
Bibkey:
Cite (ACL):
Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, and Marco Baroni. 2019. EGG: a toolkit for research on Emergence of lanGuage in Games. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 55–60, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
EGG: a toolkit for research on Emergence of lanGuage in Games (Kharitonov et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-3010.pdf
Data
MNIST