论文标题
用自我增强随机过程对新兴词典形成建模
Modeling Emergent Lexicon Formation with a Self-Reinforcing Stochastic Process
论文作者
论文摘要
我们介绍了Filex，这是一种自我增强的随机过程，该过程在新兴语言实验中建模有限的词典。 Filex的核心属性是它是一个自我增强过程，与直觉相似，即用一种语言使用单词越多，其使用越多。作为一个理论模型，Filex是解释和预测新兴语言系统行为的一种方式。我们从经验上测试了Filex捕获新兴语言的超参数与词典的香农熵之间关系的能力。
We introduce FiLex, a self-reinforcing stochastic process which models finite lexicons in emergent language experiments. The central property of FiLex is that it is a self-reinforcing process, parallel to the intuition that the more a word is used in a language, the more its use will continue. As a theoretical model, FiLex serves as a way to both explain and predict the behavior of the emergent language system. We empirically test FiLex's ability to capture the relationship between the emergent language's hyperparameters and the lexicon's Shannon entropy.
