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Emergent Communication: Towards Natural Language
Abhinav Gupta · Michael Noukhovitch · Cinjon Resnick · Natasha Jaques · Angelos Filos · Marie Ossenkopf · Angeliki Lazaridou · Jakob Foerster · Ryan Lowe · Douwe Kiela · Kyunghyun Cho

Sat Dec 14 08:00 AM -- 06:00 PM (PST) @ West 118 - 120
Event URL: https://sites.google.com/view/emecom2019 »

Communication is one of the most impressive human abilities but historically it has been studied in machine learning on confined datasets of natural language, and by various other fields in simple low-dimensional spaces. Recently, with the rise of deep RL methods, the questions around the emergence of communication can now be studied in new, complex multi-agent scenarios. Two previous successful workshops (2017, 2018) have gathered the community to discuss how, when, and to what end communication emerges, producing research that was later published at top ML venues such as ICLR, ICML, AAAI. Now, we wish to extend these ideas and explore a new direction: how emergent communication can become more like natural language, and what natural language understanding can learn from emergent communication.

The push towards emergent natural language is a necessary and important step in all facets of the field. For studying the evolution of human language, emerging a natural language can uncover the requirements that spurred crucial aspects of language (e.g. compositionality). When emerging communication for multi-agent scenarios, protocols may be sufficient for machine-machine interactions, but emerging a natural language is necessary for human-machine interactions. Finally, it may be possible to have truly general natural language understanding if agents learn the language through interaction as humans do. To make this progress, it is necessary to close the gap between artificial and natural language learning.

To tackle this problem, we want to take an interdisciplinary approach by inviting researchers from various fields (machine learning, game theory, evolutionary biology, linguistics, cognitive science, and programming languages) to participate and engaging them to unify the differing perspectives. We believe that the third iteration of this workshop with a novel, unexplored goal and strong commitment to diversity will allow this burgeoning field to flourish.

Author Information

Abhinav Gupta (Mila)
Michael Noukhovitch (Mila (Université de Montréal))

Master's student at MILA supervised by Aaron Courville and co-supervised by Yoshua Bengio

Cinjon Resnick (NYU)
Natasha Jaques (MIT)
Angelos Filos (University of Oxford)
Marie Ossenkopf (University of Kassel)

Marie Ossenkopf (Uni Kassel) is a PhD student at the University of Kassel in the Distributed Systems Group supervised by Kurt Geihs. She is currently writing her thesis on architectural necessities of emergent communication, especially for multilateral agreements. She received her MSc in Automation Engineering from RWTH Aachen University in 2016 and organizes international youth exchange workshops since 2017. She was a co-organizer of the Emergent Communication workshop at NeurIPS 2019. When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning. Ossenkopf, Marie; Jorgensen, Mackenzie; Geihs, Kurt. In: Cybernetics and Systems vol. 50, Taylor & Francis (2019), Nr. 8, pp. 672-692 Hierarchical Multi-Agent Deep Reinforcement Learning to Develop Long-Term Coordination. Ossenkopf, Marie, Mackenzie Jorgensen, and Kurt Geihs. SAC 2019.

Angeliki Lazaridou (DeepMind)
Jakob Foerster (Facebook AI Research)

Jakob Foerster received a CIFAR AI chair in 2019 and is starting as an Assistant Professor at the University of Toronto and the Vector Institute in the academic year 20/21. During his PhD at the University of Oxford, he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. He has since been working as a research scientist at Facebook AI Research in California, where he will continue advancing the field up to his move to Toronto. He was the lead organizer of the first Emergent Communication (EmeCom) workshop at NeurIPS in 2017, which he has helped organize ever since.

Ryan Lowe (McGill University)
Douwe Kiela (Facebook AI Research)
Kyunghyun Cho (New York University)

Kyunghyun Cho is an associate professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at the Université de Montréal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

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