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Exploring the Robustness of Cross-Situational Learning Under Zipfian Distributions

机译：在Zipfian分布下探究跨情境学习的稳健性

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Cross-situational learning has recently gained attention as a plausible candidate for the mechanism that underlies the learning of word-meaning mappings. In a recent study, Blythe and colleagues have studied how many trials are theoretically required to learn a human-sized lexicon using cross-situational learning. They show that the level of referential uncertainty exposed to learners could be relatively large. However, one of the assumptions they made in designing their mathematical model is questionable. Although they rightfully assumed that words are distributed according to Zipf s law, they applied a uniform distribution of meanings. In this article, Zipf s law is also applied to the distribution of meanings, and it is shown that under this condition, cross-situational learning can only be plausible when referential uncertainty is sufficiently small. It is concluded that cross-situational learning is a plausible learning mechanism but needs to be guided by heuristics that aid word learners with reducing referential uncertainty.
机译：跨情境学习作为一种潜在的词义映射学习机制的合理候选者，最近受到关注。在最近的一项研究中，Blythe及其同事研究了使用跨情境学习理论上需要多少次实验才能学习人类大小的词典。他们表明学习者面临的参照不确定性水平可能相对较大。但是，他们在设计数学模型时所做的假设之一值得怀疑。尽管他们正确地假设单词是根据Zipf的定律分布的，但是他们应用了意义的统一分布。在本文中，齐普夫定律也适用于意义的分布，并且表明在这种情况下，只有当指称不确定性足够小时，跨情境学习才可能是合理的。结论是，跨情境学习是一种合理的学习机制，但需要以启发式学习为指导，以帮助词汇学习者减少参照不确定性。

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