18148045. Description-driven Task-oriented Dialogue Modeling simplified abstract (Google LLC)

Description-driven Task-oriented Dialogue Modeling

Organization Name

Google LLC

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 18148045 titled 'Description-driven Task-oriented Dialogue Modeling

Simplified Explanation

The patent application describes methods for training a language model to predict dialog states for a given task based on input schema representation and contextual information.

Key Features and Innovation

• Input schema representation includes natural language descriptions of slot and intent descriptions.
• Contextual representation is based on a history of dialog sequences exchanged between a user and a service agent.
• Training a sequence-to-sequence language model to predict dialog states for an input 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 helps in improving the accuracy and efficiency of dialog systems by considering both the task schema and the context of the conversation.

Benefits

• Enhanced user experience in interacting with AI systems.
• More accurate and context-aware responses.
• Increased automation and efficiency in handling user queries.

Commercial Applications

• Customer service chatbots for businesses.
• Virtual assistants for various applications.
• Automated helpdesk systems for handling user queries.

Prior Art

Researchers can explore prior work on sequence-to-sequence models in natural language processing and dialog systems to understand the evolution of this technology.

Frequently Updated Research

Stay updated on advancements in natural language processing, dialog systems, and AI technologies to enhance the capabilities of this innovation.

Questions about Dialog State Prediction

How does the input schema representation impact the accuracy of dialog state prediction?

The input schema representation provides crucial information about the task, helping the model assign values to slots accurately.

What are the potential challenges in training a sequence-to-sequence language model for dialog state prediction?

Challenges may include handling complex dialog contexts, ensuring scalability, and optimizing the model for real-time interactions.

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.