不确定非均匀世界中词汇学习的随机动力学
Stochastic dynamics of lexicon learning in an uncertain and nonuniform world
摘要：
我们研究的时间由一个语言学习者正确识别的词汇中的所有单词的含义的条件下，可以推断出许多似是而非的含义，每当一个词是说出。我们发现，最基本的形式的跨情境学习--将来自多个事件的信息结合起来以消除不正确的含义--在独立学习单词和从非均匀分布中提取含义时会表现不佳。如果学习者进一步假设，没有两个单词共享一个共同的含义，我们发现一个相变之间的最高效率的学习制度，其中的学习时间减少到最短的，它可能是，和部分有效的制度，不正确的候选含义的话，坚持在晚些时候。我们通过一个等价的统计力学问题的枚举循环空间中的词义映射的单词学习过程中获得确切的结果。
英文摘要：
We study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered. We show that the most basic form of cross-situational learning - whereby information from multiple episodes is combined to eliminate incorrect meanings - can perform badly when words are learned independently and meanings are drawn from a nonuniform distribution. If learners further assume that no two words share a common meaning, we find a phase transition between a maximally-efficient learning regime, where the learning time is reduced to the shortest it can possibly be, and a partially-efficient regime where incorrect candidate meanings for words persist at late times. We obtain exact results for the word-learning process through an equivalence to a statistical mechanical problem of enumerating loops in the space of word-meaning mappings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
