arXiv:2403.14705v1 Announce Type: new
Abstract: Artificial agents that learn to communicate in order to accomplish a given task acquire communication protocols that are typically opaque to a human. A large body of work has attempted to evaluate the emergent communication via various evaluation measures, with emph{compositionality} featuring as a prominent desired trait. However, current evaluation procedures do not directly expose the compositionality of the emergent communication. We propose a procedure to assess the compositionality of emergent communication by finding the best-match between emerged words and natural language concepts. The best-match algorithm provides both a global score and a translation-map from emergent words to natural language concepts. To the best of our knowledge, it is the first time that such direct and interpretable mapping between emergent words and human concepts is provided.
Assessing the Compositionality of Emergent Communication
Evaluating the effectiveness of communication in artificial agents has been a challenging task due to the opaque nature of their learned communication protocols. While many evaluation measures have been proposed, the concept of compositionality has emerged as a crucial factor in assessing the quality of the communication.
Compositionality refers to the ability of agents to combine basic linguistic elements to express more complex meanings. It allows for flexible and efficient communication, as agents can generate a wide range of messages using a limited set of building blocks.
However, existing evaluation procedures do not directly address the issue of compositionality. This article introduces a novel procedure to evaluate the compositionality of emergent communication by establishing a direct mapping between the emerged words and natural language concepts.
The proposed evaluation procedure involves finding the best-match between emergent words used by the agents and their corresponding natural language concepts. This best-match algorithm provides a global score that quantifies the level of compositionality achieved by the agents, as well as a translation-map that links emergent words to human concepts.
This approach is significant in two ways. First, it allows for a direct and interpretable mapping between the emergent communication and human concepts. This enables researchers to gain deeper insights into the semantic content of the learned communication protocols and understand the emergence of compositionality.
Second, this procedure is multi-disciplinary in nature. It bridges the gap between natural language processing and machine learning, as it combines linguistic concepts with methods from artificial intelligence and communication research. By integrating insights from these multiple disciplines, the evaluation procedure provides a more comprehensive understanding of the compositionality of emergent communication.
In conclusion, the proposed procedure offers a novel and interpretable approach to evaluating compositionality in emergent communication. By establishing a direct mapping between emergent words and natural language concepts, researchers can gain deeper insights into the communication capabilities of artificial agents and foster further progress in the development of advanced communication protocols.