  Cross-situational word learning, wherein a learner combines information about
possible meanings of a word across multiple exposures, has previously been
shown to be a very powerful strategy to acquire a large lexicon in a short
time. However, this success may derive from idealizations that are made when
modeling the word-learning process. In particular, an earlier model assumed
that a learner could perfectly recall all previous instances of a word's use
and the inferences that were drawn about its meaning. In this work, we relax
this assumption and determine the performance of a model cross-situational
learner who forgets word-meaning associations over time. Our main finding is
that it is possible for this learner to acquire a human-scale lexicon by
adulthood with word-exposure and memory-decay rates that are consistent with
empirical research on childhood word learning, as long as the degree of
referential uncertainty is not too high or the learner employs a mutual
exclusivity constraint. Our findings therefore suggest that successful word
learning does not necessarily demand either highly accurate long-term tracking
of word and meaning statistics or hypothesis-testing strategies.

Physics and Society;Computation and Language