Generalisation towards Combinatorial Productivity in Language Acquisition by Simple Recurrent Networks

F.C.K. Wong, W. Wang
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引用次数: 13

Abstract

Language exhibits combinatorial productivity as complex constructions are composed of simple elements in a linear or hierarchical fashion. Complexity arises as one cannot be exposed to all possible combinations during ontogeny and yet to master a language one need to be, and very often is, able to generalise to process and comprehend constructions that are of novel combinations. Accounting for such an ability is a current challenge being tackled in connectionist research. In this study, we will first demonstrate that connectionist networks do generalise towards combinatorial productivity followed by an investigation of how the networks could achieve that
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简单递归网络在语言习得中的组合生产力推广
语言表现出组合生产力,因为复杂的结构是由简单的元素以线性或分层的方式组成的。在个体发育过程中,一个人不可能接触到所有可能的组合,而要掌握一门语言,一个人需要,而且经常能够概括地处理和理解由新组合组成的结构,这就产生了复杂性。解释这种能力是连接主义研究中目前正在解决的一个挑战。在这项研究中,我们将首先证明连接主义网络确实可以推广到组合生产力,然后调查网络如何实现这一目标
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