Evolving Learnable Languages

Recent theories suggest that language acquisition is assisted by the evolution of languages towards forms that are easily learnable. In this paper, we evolve combinatorial languages which can be learned by a recurrent neural network quickly and from relatively few examples. Additionally,we evolve languages for generalization in different "worlds", and for generalization from specific examples. We find that languages can be evolved to facilitate different forms of impressive generalization for a minimally biased, general purpose learner.The results provide empirical support for the theory that the language itself, as well as the language environment of a learner, plays a substantial role in learning: that there is far more to language acquisition than the language acquisition device. 1 Introduction: Factors in language learnability In exploring issues oflanguage learnability, the special abilities of humans to learn complex languages have been much emphasized, with one dominant theory based on innate, domain-specific learning mechanisms specifically tuned to learning human languages.It has been argued that without strong constraints on the learning mechanism, the complex syntax of language could .not