Google llc (20240220732). Description-driven Task-oriented Dialogue Modeling simplified abstract
Contents
Description-driven Task-oriented Dialogue Modeling
Organization Name
Inventor(s)
Raghav Gupta of Mountain View CA (US)
Yuan Cao of Mountain View CA (US)
Abhinav Kumar Rastogi of Mountain View CA (US)
Harrison J. Lee of Seattle WA (US)
Jeffrey Liangjie Zhao of Mountain View CA (US)
Description-driven Task-oriented Dialogue Modeling - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240220732 titled 'Description-driven Task-oriented Dialogue Modeling
Simplified Explanation: The patent application describes methods for training a sequence-to-sequence language model to predict dialog states for a given task based on input schema representation and contextual information.
Key Features and Innovation:
- Determining input schema representation with natural language descriptions of slot and intent descriptions.
- Creating a contextual representation based on dialog sequences exchanged between a user and a service agent.
- Training a sequence-to-sequence language model to predict dialog states for a task.
- Providing the trained language model for use in generating dialog responses.
Potential Applications: This technology can be applied in chatbots, virtual assistants, customer service automation, and other conversational AI systems.
Problems Solved: This technology addresses the challenge of understanding user preferences and context in natural language interactions.
Benefits:
- Improved accuracy in predicting user preferences.
- Enhanced user experience in dialog-based interactions.
- Efficient handling of complex tasks in conversational AI systems.
Commercial Applications: The technology can be utilized in customer service platforms, e-commerce chatbots, virtual assistants for businesses, and other AI-driven communication tools.
Prior Art: Researchers can explore prior work in natural language processing, dialog systems, and sequence-to-sequence models for related technologies.
Frequently Updated Research: Stay updated on advancements in natural language understanding, dialog state tracking, and machine learning techniques for conversational AI.
Questions about Conversational AI: 1. How does this technology improve user engagement in conversational interfaces? 2. What are the key challenges in training sequence-to-sequence language models for dialog state prediction?
Original Abstract Submitted
example methods include determining an input schema representation for a task. the schema representation comprises natural language descriptions of slot and intent descriptions, wherein respective indices are associated with each of the slot descriptions and each of the intent descriptions. the methods include determining a contextual representation comprising a concatenation of a history of dialog sequences exchanged between a user and a service agent, wherein the dialog sequences describe a context for the task. the methods include training, a sequence-to-sequence language model and based on a concatenation of the input schema representation and the contextual representation, to predict a sequence of dialog states for an input task, wherein the sequence of dialog states comprises an assignment of values to slots for which the user has indicated a preference in dialog sequences corresponding to the input task. the methods include providing the trained sequence-to-sequence language model.
