Referential games offer a grounded learning environment for neural agents , that accounts for the functional aspects of language . However, they fail to account for another fundamental aspect of human language: Because languages are transmitted from generation to generation, they have to be learnable by new language users, which makes them subject to cultural evolution. Recent work has shown that incorporating cultural evolution in referential game results in considerable improvements in the properties of the languages that emerge in the game . In this work, we first substantiate this claim with a different data set and a wider array of evaluation metrics. Then, drawing inspiration from linguistic theories of human language evolution, we consider a scenario in which not only cultural but also genetic evolution is integrated. As our core contribution, we introduce the Language Transmission Engine, in which cultural evolution of the language is combined with genetic evolution of the agents' architecture. We show that this co-evolution scenario leads to across-the-board improvements on all considered metrics  . These results stress that cultural evolution is important for language emergence studies, but also the suitability of the architecture itself should be considered .
Referential games offer a grounded learning environment for neural agents which accounts for the fact that language is functionally used to communicate . However, they do not take into account a second constraint considered to be fundamental for the shape of human language: that it must be learnable by new language learners. Cogswell et al. (2019) introduced cultural transmission within referential games through a changing population of agents to constrain the emerging language to be learnable. However, the resulting languages remain inherently biased by the agents' underlying capabilities . In this work, we introduce Language Transmission Engine to model both cultural and architectural evolution in a population of agents. As our core contribution, we empirically show that the optimal situation is to take into account also the learning biases of the language learners and thus let language and agents co-evolve. When we allow the agent population to evolve through architectural evolution, we achieve across the board improvements on all considered metrics and surpass the gains made with cultural transmission . These results stress the importance of studying the underlying agent architecture and pave the way to investigate the co-evolution of language and agent in language emergence studies .
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functional aspects of language
fact that language is functionally used to communicate
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fail to account for another fundamental aspect
do not take into account a second constraint considered to be fundamental for the shape
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Because languages are transmitted from generation to generation, they have to
that it must
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users, which makes them subject to cultural evolution. Recent work has shown that incorporating cultural evolution in referential game results in considerable improvements in the properties of the languages that emerge in the game
learners. Cogswell et al. (2019) introduced cultural transmission within referential games through a changing population of agents to constrain the emerging language to be learnable. However, the resulting languages remain inherently biased by the agents' underlying capabilities
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first substantiate this claim with a different data set and a wider array of evaluation metrics. Then, drawing inspiration from linguistic theories of human language evolution, we consider a scenario in which not only cultural but also genetic evolution is integrated.
introduce Language Transmission Engine to model both cultural and architectural evolution in a population of agents.
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introduce the Language Transmission Engine, in which cultural evolution of the language is combined with genetic evolution of the agents' architecture. We show that this co-evolution scenario leads to across-the-board
empirically show that the optimal situation is to take into account also the learning biases of the language learners and thus let language and agents co-evolve. When we allow the agent population to evolve through architectural evolution, we achieve across the board
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and surpass the gains made with cultural transmission
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that cultural evolution is important for language emergence studies, but also the suitability of
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architecture itself should be considered
importance of studying the underlying agent architecture and pave the way to investigate the co-evolution of language and agent in language emergence studies