The semantic bootstrapping hypothesis proposes that children acquire their native language through exposure to sentences of the language paired with structured representations of their meaning, whose component substructures can be associated with words and syntactic structures used to express these concepts. The child's task is then to learn a language-specific grammar and lexicon based on (probably contextually ambiguous, possibly somewhat noisy) pairs of sentences and their meaning representations (logical forms). Starting from these assumptions, we develop a Bayesian probabilistic account of semantically bootstrapped first-language acquisition in the child, based on techniques from computational parsing and interpretation of unrestricted text. Our learner jointly models (a) word learning: the mapping between components of the given sentential meaning and lexical words (or phrases) of the language, and (b) syntax learning: the projection of lexical elements onto sentences by universal construction-free syntactic rules. Using an incremental learning algorithm, we apply the model to a dataset of real syntactically complex child-directed utterances and (pseudo) logical forms, the latter including contextually plausible but irrelevant distractors. Taking the Eve section of the CHILDES corpus as input, the model simulates several well-documented phenomena from the developmental literature. In particular, the model exhibits syntactic bootstrapping effects (in which previously learned constructions facilitate the learning of novel words), sudden jumps in learning without explicit parameter setting, acceleration of word-learning (the "vocabulary spurt"), an initial bias favoring the learning of nouns over verbs, and one-shot learning of words and their meanings. The learner thus demonstrates how statistical learning over structured representations can provide a unified account for these seemingly disparate phenomena.

中文翻译：

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引导语言习得。

语义引导假说提出，儿童通过接触与其含义的结构化表示配对的语言句子来获取母语，其组成子结构可以与用于表达这些概念的单词和句法结构相关联。然后，孩子的任务是根据（可能是上下文模棱两可，可能有些嘈杂的）句子对和它们的意思表示（逻辑形式）来学习特定语言的语法和词典。从这些假设出发，我们基于无限制文本的计算分析和解释技术，开发了儿童语义引导的第一语言习得的贝叶斯概率帐户。我们的学习者共同模拟（a）单词学习：给定的句子含义的组成部分与语言的词汇词（或短语）之间的映射，以及（b）语法学习：通过通用的无构造句法规则将词汇元素投射到句子上。使用增量学习算法，我们将该模型应用于实际语法复杂的儿童定向话语和（伪）逻辑形式的数据集，后者包括上下文合理的但无关紧要的干扰词。该模型以CHILDES语料的Eve部分作为输入，该模型模拟了来自发展文献的一些有据可查的现象。尤其是，该模型具有句法引导效果（其中，先前学习的构造有助于学习新颖的单词），在没有显式参数设置的情况下学习突然跳跃，单词学习加速（“