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Abstract

Lexicon learning systems need to be concerned with more than just producing symbol usage agreement between agents, which is easy to acquire through imitation. Lexicon learners should also explicitly attempt to increase the mutual information between their symbol usages (a measure of the usefulness of the symbols for transferring information between agents). This paper argues that, although many lexicon learning algorithms presented in the literature do attempt to create highly informative symbol usages implicitly, there are good reasons to make the mutual information of symbol usages an explicit goal of the lexicon learning system. Some first steps in this direction are provided in this paper. It presents lexicon learning experiments using both purely imitative and explicitly information maximizing algorithms. The results of these experiments are used to support the thesis of this paper, that lexicon learning algorithms should explicitly attempt to produce high mutual information symbol usages.

@inproceedings{fleischer04SAB,
  author={Jason Fleischer and Jonathan Shapiro},
  title={Imitation Is Not Enough for Lexicon Learning},
  year={2004},
  pages={477-486},
  booktitle={SAB04},
  url={http://www.isrl.uiuc.edu/~amag/langev/paper/fleischer04SAB.html}
}

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