Learning Communicative Acts in Children's Conversations: A Hidden Topic Markov Model Analysis of the CHILDES Corpora.

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. Characterizing the dynamics of children's conversations across development, using a Hidden Topic Markov Model to classify parents' and children's communicative acts and analyze their sequential structure in conversation. [ABSTRACT FROM AUTHOR]