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首页> 外文期刊>Connection Science >Strong systematicity through sensorimotor conceptual grounding: an unsupervised, developmental approach to connectionist sentence processing
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Strong systematicity through sensorimotor conceptual grounding: an unsupervised, developmental approach to connectionist sentence processing

机译：通过感觉运动概念的扎实实现强大的系统性：一种无监督的发展方法，用于连接句处理

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摘要

Connectionist language modelling typically has difficulty with syntactic systematicity, or the ability to generalise language learning to untrained sentences. This work develops an unsupervised connectionist model of infant grammar learning. Following the semantic boostrapping hypothesis, the network distils word category using a developmentally plausible infant-scale database of grounded sensorimotor con-ceptual representations, as well as a biologically plausible semantic co-occurrence activation function. The network then uses this knowledge to acquire an early benchmark clausal grammar using correla-tional learning, and further acquires separate conceptual and grammatical category representations. The network displays strongly systematic behaviour indicative of the general acquisition of the combinatorial systematicity present in the grounded infant-scale language stream, outperforms previous contemporary models that contain primarily noun and verb word categories, and successfully generalises broadly to novel untrained sensorimotor grounded sentences composed of unfamiliar nouns and verbs. Limitations as well as implications to later grammar learning are discussed.
机译：连接主义语言建模通常在句法系统性或将语言学习推广到未经训练的句子的能力方面有困难。这项工作建立了婴儿语法学习的无监督连接模型。遵循语义增强假设，网络使用接地的感觉运动概念表示形式的发展合理的婴儿规模数据库以及生物学合理的语义共现激活功能来分配单词类别。然后，网络使用此知识通过相关学习来获取早期的基准子句语法，并进一步获取单独的概念和语法类别表示。该网络显示出强烈的系统行为，表明存在基本的以婴儿为母语的语言流中的组合系统性的一般习得，优于主要包含名词和动词类别的先前的现代模型，并成功地广泛推广到由以下组成的新型未经训练的感觉运动基础句子：不熟悉的名词和动词。讨论了局限性以及对以后语法学习的影响。

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