Papers by Angeliki Lazaridou

3 papers
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning (2020.acl-main)

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Challenge: a new method for combining multi-agent communication with traditional data-driven approaches to natural language learning is proposed . we combine the two types of learning with a goal of teaching agents to communicate with humans in natural language.
Approach: They propose a method that combines traditional data-driven approaches to natural language learning with multi-agent self-play environments.
Outcome: The proposed method outperforms other methods in communicating with humans in natural language.
Experience Grounds Language (2020.emnlp-main)

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Challenge: aaron carroll: language understanding research is held back by a failure to relate language to the physical world it describes and to social interactions it facilitates. carroll says successful linguistic communication relies on a shared experience of the world.
Approach: They propose to use a broader physical and social context to address communication problems . they argue that the current success of representation learning approaches is limited .
Outcome: a new study suggests that the current success of representation learning requires a parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.
Emergent Language-Based Coordination In Deep Multi-Agent Systems (2022.emnlp-tutorials)

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Challenge: Pre-trained deep networks are the standard building blocks of modern AI applications.
Approach: This tutorial will introduce deep net emergent communication and discuss current shortcomings . participants will implement and analyze two emergentic communication setups from the literature .
Outcome: The presentation will cover various topics from the present and recent past, as well as discussing current shortcomings and suggest future directions.

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