论文标题
跨性别单词学习中的竞争：一项计算研究
Competition in Cross-situational Word Learning: A Computational Study
论文作者
论文摘要
孩子们通过在使用单词的不同情况下利用共同点来学习单词含义，并克服早期单词学习经历中涉及的高度不确定性。我们提出了一个建模框架，以调查相互排他性偏见的作用 - 在降低单词学习中的不确定性中，主张单词及其含义之间的一对一映射。在一系列计算研究中，我们表明，要在面对不确定性的情况下成功学习单词含义，学习者需要使用两种类型的竞争：在使用观察结果和参与者竞争单词时，在使用该单词时竞争与参考的竞争。我们的工作强调了算法级别分析对可以实现相同计算级理论的不同机制的实用性的重要性。
Children learn word meanings by tapping into the commonalities across different situations in which words are used and overcome the high level of uncertainty involved in early word learning experiences. We propose a modeling framework to investigate the role of mutual exclusivity bias - asserting one-to-one mappings between words and their meanings - in reducing uncertainty in word learning. In a set of computational studies, we show that to successfully learn word meanings in the face of uncertainty, a learner needs to use two types of competition: words competing for association to a referent when learning from an observation and referents competing for a word when the word is used. Our work highlights the importance of an algorithmic-level analysis to shed light on the utility of different mechanisms that can implement the same computational-level theory.
