Modeling Intent, Dialog Policies and Response Adaptation for Goal-Oriented Interactions
This work addresses the challenge of creating engaging and effective dialog systems for children's interactions, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of building a robust spoken dialog system for goal-oriented interactions with children, focusing on intent recognition and dialog policy learning, and reports that their bootstrapped models outperform baseline approaches in intent recognition and dialog action prediction.
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.