核心概念
Emergent communication compositionality can be assessed through a best-match algorithm, providing insights into the mapping between emergent words and natural language concepts.
要約
The content discusses evaluating compositionality in emergent communication, proposing a method to assess the compositionality of emergent communication by finding the best match between emerged words and natural language concepts. The article outlines the challenges in quantifying compositionality and introduces a procedure based on a best-match algorithm. Experimental setups, datasets, and results are detailed, showcasing how different communication types impact accuracy and compositionality assessment.
Structure:
- Abstract: Discusses the opacity of communication protocols in artificial agents.
- Introduction: Introduces artificial agents learning to communicate for tasks.
- Concept Best Matching: Proposes a method to assess compositionality through a best-match algorithm.
- Background: Details emergent communication setup and measures of compositionality evaluation.
- Results: Presents experimental setups, datasets used, and results from various configurations.
- Conclusion: Summarizes the proposed procedure for assessing compositionality in emergent communication.
- Limitations: Outlines limitations of the analysis concerning dataset requirements and assumptions made during evaluation.
統計
"A large body of work has attempted to evaluate the emergent communication via various evaluation measures."
"Our approach provides detailed insights into the reasons for sub-optimal translation."
"QT was shown to be superior and easier to optimize compared to GS in EC games."