Evolving Learnable Languages
Abstract
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 ex(cid:173) amples. 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 pur(cid:173) pose 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.
Cite
Text
Tonkes et al. "Evolving Learnable Languages." Neural Information Processing Systems, 1999.Markdown
[Tonkes et al. "Evolving Learnable Languages." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/tonkes1999neurips-evolving/)BibTeX
@inproceedings{tonkes1999neurips-evolving,
title = {{Evolving Learnable Languages}},
author = {Tonkes, Bradley and Blair, Alan and Wiles, Janet},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {66-72},
url = {https://mlanthology.org/neurips/1999/tonkes1999neurips-evolving/}
}