RubyStar: A Non-Task-Oriented Mixture Model Dialog System
arXiv:1711.02781
Abstract
RubyStar is a dialog system designed to create "human-like" conversation by combining different response generation strategies. RubyStar conducts a non-task-oriented conversation on general topics by using an ensemble of rule-based, retrieval-based and generative methods. Topic detection, engagement monitoring, and context tracking are used for managing interaction. Predictable elements of conversation, such as the bot's backstory and simple question answering are handled by separate modules. We describe a rating scheme we developed for evaluating response generation. We find that character-level RNN is an effective generation model for general responses, with proper parameter settings; however other kinds of conversation topics might benefit from using other models.
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Cited by in corpus (6)
- Conversational AI: The Science Behind the Alexa Prize
- Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
- Proposal Towards a Personalized Knowledge-powered Self-play Based Ensemble Dialog System
- Building A User-Centric and Content-Driven Socialbot
- Data-Efficient Methods for Dialogue Systems
- Efficient Purely Convolutional Text Encoding