  Contextually aware intelligent agents are often required to understand the
users and their surroundings in real-time. Our goal is to build Artificial
Intelligence (AI) systems that can assist children in their learning process.
Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial
building blocks to handle efficient task-oriented communication with children
in game-based learning settings. We are working towards a multimodal dialogue
system for younger kids learning basic math concepts. Our focus is on improving
the Natural Language Understanding (NLU) module of the task-oriented SDS
pipeline with limited datasets. This work explores the potential benefits of
data augmentation with paraphrase generation for the NLU models trained on
small task-specific datasets. We also investigate the effects of extracting
entities for conceivably further data expansion. We have shown that
paraphrasing with model-in-the-loop (MITL) strategies using small seed data is
a promising approach yielding improved performance results for the Intent
Recognition task.
