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探索概率语法在语言易学性和儿童沟通上的作用

Explorations in Language Learnability Using Probabilistic Grammars and Child-directed Speech
课程网址: http://videolectures.net/mitworld_tenenbaum_gcds/  
主讲教师: Joshua B. Tenenbaum
开课单位: 麻省理工学院
开课时间: 2012-02-10
课程语种: 英语
中文简介:
孩子们如何设法弄清楚“狗”这个词适用于整个动物类别,而不仅仅是一个动物? Joshua Tenenbaum希望了解儿童和成人如何设法解决这种经典的归纳问题。在整个认知过程中,无论你在哪里看,他都说“我们看到的地方比我们对世界有合理的了解权利,我们来到抽象的地方,概括,超越我们稀疏,嘈杂的世界模型,有限的经验。“Tenenbaum的目标是提出”通用计算工具,用于理解人们如何成功地解决这些问题。“他正在创建一组分层的概率模型,这将有助于解释人类如何实现归纳性飞跃 - 如何抽象知识“引导和约束我们的推论”帮助我们从最初的日子获得语言。虽然他的模型可以应用于许多认知领域,但Tenenbaum专注于最近的语法工作。从非常简单的数据来看,孩子们设法将一个复杂的陈述,如“正在睡觉的女孩是幸福的”,变成一个复杂的疑问:“睡觉的女孩是幸福的吗?”他们没有说,“是睡觉的女孩吗?很高兴?“Tenenbaum建议人类以某种方式识别语言的层次短语结构,并将其用作”归纳约束来指导特定句法的获取。“Tenenbaum和他的同事使用来自儿童指导语音的数据建立了代表性语法2300个句子,相当于2万多个话语。他解构了这些句子,使每个单词都被句法范畴所取代。 “婴儿熊在他的床上发现了金发姑娘”变成了“预先调整”。他正在研究这些语法,因为它们能够平衡复杂性,适当推广和适应数据的能力。他的结果表明“通过具有正确的归纳偏见,分层短语结构的概念,你可以做出你没有证据的概括......”
课程简介: How do kids manage to figure out that the word “dog” applies to a whole category of animals, not just one creature? Joshua Tenenbaum wants to understand how children and adults manage to solve such classic problems of induction. Throughout cognition, wherever you look, he says “we see places where we know more than we have a reasonable right to know about the world, places where we come to abstractions, generalizations, models of the world that go beyond our sparse, noisy, limited experience.” Tenenbaum’s goal is to come up with “general purpose computational tools for understanding how people solve these problems so successfully.” He’s creating a set of hierarchical, probabilistic models that will help explain how humans make inductive leaps – how abstract knowledge that “guides and constrains our inferences” helps us acquire language from our earliest days. While his models can apply to many areas of cognition, Tenenbaum focuses on recent work with syntax. From very simple data, children manage to turn a complex declarative like “The girl who is sleeping is happy,” to a complex interrogative: “Is the girl who is sleeping happy?” They don’t say, “Is the girl who sleeping is happy?” Tenenbaum suggests that humans somehow identify the hierarchical phrase structure of language, and use this as an “inductive constraint to guide acquisition of a particular piece of syntax.” Tenenbaum and his colleagues have built representative grammars using data from child-directed speech --2300 sentences that correspond to 20 thousand-plus utterances. He deconstructs these sentences so that each word is replaced by a syntactic category. “The baby bear discovers Goldilocks in his bed” becomes “det adj n v prop pre adj n.” He’s explored these grammars for their capacity to balance complexity, generalize appropriately, and ability to fit the data. His results indicate that “by having the right kind of inductive bias, the idea of hierarchical phrase structure, you can make generalizations which you have no evidence for…”
关 键 词: 认知过程; 分层概率模型; 分层短语结构; 代表性语法
课程来源: 视频讲座网
最后编审: 2020-06-08:cxin
阅读次数: 52