Context Aware Conversational Understanding for Intelligent Agents With a Screen

Authors

  • Vishal Naik Arizona State University
  • Angeliki Metallinou Amazon
  • Rahul Goel Amazon

Keywords:

intelligent conversational agents, context, screen integration, spoken language understanding, deep neural networks

Abstract

We describe an intelligent context-aware conversational system that incorporates screen context information to service multimodal user requests. Screen content is used for disambiguation of utterances that refer to screen objects and for enabling the user to act upon screen objects using voice commands. We propose a deep learning architecture that jointly models the user utterance and the screen and incorporates detailed screen content features. Our model is trained to optimize end to end semantic accuracy across contextual and non-contextual functionality, therefore learns the desired behavior directly from the data. We show that this approach outperforms a rule-based alternative, and can be extended in a straightforward manner to new contextual use cases. We perform detailed evaluation of contextual and non-contextual use cases and show that our system displays accurate contextual behavior without degrading the performance of non-contextual user requests.

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Published

2018-04-27

How to Cite

Naik, V., Metallinou, A., & Goel, R. (2018). Context Aware Conversational Understanding for Intelligent Agents With a Screen. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11952