Modeling discussion topics in interactions with a tablet reading primer

Abstract

CloudPrimer is a tablet-based interactive reading primer that aims to foster early literacy skills and shared parent-child reading through user-targeted discussion topic suggestions. The tablet application records discussions between parents and children as they read a story and leverages this information, in combination with a common sense knowledge base, to develop discussion topic models. The long-term goal of the project is to use such models to provide context-sensitive discussion topic suggestions to parents during the shared reading activity in order to enhance the interactive experience and foster parental engagement in literacy education. In this paper, we present a novel approach for using commonsense reasoning to effectively model topics of discussion in unstructured dialog. We introduce a metric for localizing concepts that the users are interested in at a given moment in the dialog and extract a time sequence of words of interest. We then present algorithms for topic modeling and refinement that leverage semantic knowledge acquired from ConceptNet, a commonsense knowledge base. We evaluate the performance of our algorithms using transcriptions of audio recordings of parentchild pairs interacting with a tablet application, and compare the output of our algorithms to human-generated topics. Our results show that words of interest and discussion topics selected by our algorithm closely match those identified by human readers.

Fostering parent–child dialog through automated discussion suggestions

“…The topic modeling method used in this work builds on our previous work, incorporating common sense reasoning in evaluating topics occurring in free-form the discussions (Boteanu and Chernova 2013a). By using semantic network topology, our method can include infrequent words in the resulting topic models, provided that these words are present in the semantic network.…”

“…In prior work, we introduced an interest metric heuristic to determine which words may be of interest to the readers at any point during the session (Boteanu and Chernova 2013a). This method was designed to select words from a very small number of reading sessions, potentially producing user-specific vocabularies from single reading sessions.…”