在线迭代学习任务中语言结构的出现

IF 2.1 0 LANGUAGE & LINGUISTICS
Clay Beckner, J. Pierrehumbert, J. Hay
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引用次数: 34

摘要

Kirby, Cornish和Smith(2008)之前的研究发现,通过对人工语言的反复学习,可以在实验室中开发出引人注目的组合语言系统。然而,我们对数据的重新分析表明,虽然反复学习促进了语言组合性的增加,但这种增加之后会出现明显的下降。这种组合性的下降是无法解释的,似乎是由一个小数据集(4个传输链)中的偶然事件引起的。因此,目前的研究使用亚马逊土耳其机器人在更大范围内研究语言结构的迭代出现,包括超过10代的24个独立的学习者链。这个更丰富的数据集提供了进一步的证据,证明迭代学习导致语言变得更有组成性,尽管这种趋势在第10代之前趋于平稳。此外,对数据的分析(以及对Kirby, Cornish & Smith, 2008年的重新分析)表明,系统单位在某些意义维度上先于其他维度出现,从而深入了解学习者的偏见。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The emergence of linguistic structure in an online iterated learning task
Previous research by Kirby, Cornish & Smith (2008) has found that strikingly compositional language systems can be developed in the laboratory via iterated learning of an artificial language. However, our reanalysis of the data indicates that while iterated learning prompts an increase in language compositionality, the increase is followed by an apparent decrease. This decrease in compositionality is inexplicable, and seems to arise from chance events in a small dataset (4 transmission chains). The current study thus investigates the iterated emergence of language structure on a larger scale using Amazon Mechanical Turk, encompassing 24 independent chains of learners over 10 generations. This richer dataset provides further evidence that iterated learning causes languages to become more compositional, although the trend levels off before the 10th generation. Moreover, analysis of the data (and reanalysis of Kirby, Cornish & Smith, 2008) reveals that systematic units arise along some meaning dimensions before others, giving insight into the biases of learners.
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来源期刊
Journal of Language Evolution
Journal of Language Evolution Social Sciences-Linguistics and Language
CiteScore
4.50
自引率
7.70%
发文量
8
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