作为推理计算度量的程度估计

Eszter Ronai, M. Xiang
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引用次数: 0

摘要

标量推理(Scalar inference, SI)是语义语用学研究的一个核心问题,即包含量词“某些”的话语被充实为“某些”而不是“全部”。最近对实验文献感兴趣的是标量多样性现象:不同的词汇尺度表现出的变化是它们导致SI的可能性。然而,标量多样性的研究几乎完全依赖于一个特定的实验任务:推理任务。在本文中,我们认为推理任务存在许多缺点:即,它通过向参与者提供更强的选择而产生偏差,并且它模糊了除SI之外的语用推理。相反,我们提供了一个替代度估计任务来调查包含标量项的话语。我们使用程度估计任务来重新评估先前基于推理任务的发现,这些发现来自文献中关于两种操作(话语上下文和仅)如何影响推理计算的可能性。我们的研究结果表明,这两项任务产生的结果在微妙但重要的方面有所不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Degree estimates as a measure of inference calculation
Scalar inference (SI), e.g., utterances containing the quantifier some being enriched to mean some but not all, is a central topic in semantics-pragmatics. Of recent interest in the experimental literature is the phenomenon of scalar diversity: that different lexical scales exhibit variation is how likely they are to lead to SI. However, studies of scalar diversity have almost exclusively relied on a particular experimental task: the inference task. In this paper, we argue that the inference task suffers from a number of shortcomings: namely, that it biases by providing participants with the stronger alternative and that it obscures pragmatic inferences other than SI. Instead we offer as an alternative a degree estimate task to investigate utterances containing scalar terms. We use the degree estimate task to reassess previous inference task-based findings from the literature on how two manipulations (discourse context and only) affect the likelihood of inference calculation. Our results show that the two tasks produce results that differ from each other in subtle but important ways.
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