  Building a machine learning driven spoken dialog system for goal-oriented
interactions involves careful design of intents and data collection along with
development of intent recognition models and dialog policy learning algorithms.
The models should be robust enough to handle various user distractions during
the interaction flow and should steer the user back into an engaging
interaction for successful completion of the interaction. In this work, we have
designed a goal-oriented interaction system where children can engage with
agents for a series of interactions involving `Meet \& Greet' and `Simon Says'
game play. We have explored various feature extractors and models for improved
intent recognition and looked at leveraging previous user and system
interactions in novel ways with attention models. We have also looked at dialog
adaptation methods for entrained response selection. Our bootstrapped models
from limited training data perform better than many baseline approaches we have
looked at for intent recognition and dialog action prediction.
