Over their first years of life, children learn not just the words of their native languages, but how to use them to communicate. Because manual annotation of communicative intent does not scale to large corpora, our understanding of communicative act development is limited to case studies of a few children at a few time points. We present an approach to automatic identification of communicative acts using a hidden topic Markov model, applying it to the conversations of English-learning children in the CHILDES database. We first describe qualitative changes in parent–child communication over development, and then use our method to demonstrate two large-scale features of communicative development: (a) children develop a parent-like repertoire of our model's communicative acts rapidly, their learning rate peaking around 14 months of age, and (b) this period of steep repertoire change coincides with the highest predictability between parents' acts and children's, suggesting that structured interactions play a role in learning to communicate.

中文翻译：

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学习儿童对话中的交际行为：儿童语料库的隐藏主题马尔可夫模型分析

在他们生命的最初几年，孩子们不仅学习母语单词，而且学习如何使用它们进行交流。因为交际意图的人工注释不能扩展到大型语料库，我们对交际行为发展的理解仅限于几个孩子在几个时间点的案例研究。我们提出了一种使用隐藏主题马尔可夫模型自动识别交际行为的方法，并将其应用于 CHILDES 数据库中学习英语的儿童的对话。我们首先描述了亲子交流在发展过程中的质变，然后用我们的方法展示了交流发展的两个大规模特征：（a）儿童迅速发展出我们模型的交流行为的类似父母的全部内容，他们的学习率达到峰值大约14个月大，