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首页> 外文会议>Annual conference of the International Speech Communication Association;INTERSPEECH 2010 >Language acquisition and cross-modal associations: Computational simulation of the result of infant studies
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Language acquisition and cross-modal associations: Computational simulation of the result of infant studies

机译：语言习得和跨模态关联：婴儿研究结果的计算模拟

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摘要

This paper discusses recent results obtained with a computational model of language acquisition. In previous papers, this model, developed in the ACORNS project, has shown to be able to learn word-like units from stimuli in which utterances are paired with visual information. In this paper we extend the ACORNS experiments to the case where utterances are paired with a ambiguous visual representation, as to obtain a computational correlate of the findings by Smith and Yu in 2008. Smith and Yu stipulate that a young infant is confronted with an uncertainty problem, how to pair a word, embedded in a sentence, and a referent, embedded in a rich visual scene. They show that young infants can resolve the uncertainty problem by evaluating the statistical evidence across many individually ambiguous words and scenes. We investigate to what extent the ACORNS model is able to deal with cross-modal ambiguity. Moreover, we show the positive effect of an 'active' role during learning when confronted with ambiguity, based on internal confidence.
机译：本文讨论了使用语言习得的计算模型获得的最新结果。在以前的论文中，该模型是在ACORNS项目中开发的，已被证明能够从发声与视觉信息配对的刺激中学习类似单词的单位。在本文中，我们将ACORNS实验扩展到话语与歧义视觉表示配对的情况，以获取Smith和Yu在2008年的发现的计算相关性。Smith和Yu规定年幼婴儿面临不确定性问题，如何将嵌入句子中的单词和嵌入丰富视觉场景中的指示对象配对。他们表明，幼儿可以通过评估许多单独模棱两可的单词和场景中的统计证据来解决不确定性问题。我们调查ACORNS模型在多大程度上能够处理跨模式歧义。此外，基于内部信心，当面对歧义时，我们展示了学习过程中“主动”角色的积极作用。

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