Google llc (20240221731). Demonstration-driven Scalable Task-oriented Dialogue Modeling simplified abstract
Contents
Demonstration-driven Scalable 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)
Demonstration-driven Scalable Task-oriented Dialogue Modeling - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240221731 titled 'Demonstration-driven Scalable Task-oriented Dialogue Modeling
Simplified Explanation: The patent application describes methods for training a language model to predict dialog states based on input prompts and contextual representations in a conversation between a user and a service agent.
Key Features and Innovation:
- Determining input prompts labeled with slot-value pairs to indicate possible slots and values in the utterance.
- Creating a contextual representation by concatenating a history of utterances exchanged between the user and service agent.
- Training a sequence-to-sequence language model to predict dialog states for an input task based on the input prompt and contextual representation.
- Providing the trained language model to assign values to slots based on user preferences in dialog sequences.
Potential Applications: This technology can be applied in customer service chatbots, virtual assistants, and automated helpdesk systems to improve the accuracy and efficiency of interactions.
Problems Solved: The technology addresses the challenge of understanding user preferences and context in natural language conversations to provide more personalized and effective responses.
Benefits:
- Enhanced user experience through more accurate and contextually relevant responses.
- Increased efficiency in handling user queries and tasks.
- Improved scalability and consistency in customer service interactions.
Commercial Applications: The technology can be utilized in various industries such as e-commerce, telecommunications, and healthcare to streamline customer support processes and enhance user satisfaction.
Prior Art: Prior research in natural language processing and dialogue systems can provide insights into similar approaches to modeling dialog states and context in conversations.
Frequently Updated Research: Stay updated on advancements in natural language processing, machine learning, and conversational AI to enhance the performance and capabilities of the language model.
Questions about Language Model Training: 1. How does the language model handle ambiguous slot-value pairs in the input prompts? 2. What techniques are used to ensure the language model captures the context of the conversation accurately?
Original Abstract Submitted
example methods include determining an input prompt comprising an utterance labeled with a sequence of slot-value pairs, wherein the sequence of slot-value pairs indicates possible slots and values in the utterance, and wherein the utterance relates to a task. the methods include determining a contextual representation comprising a concatenation of a history of utterances exchanged between a user and a service agent. the utterances describe a context for the task. the methods include training, based on a concatenation of the input prompt and the contextual representation, a sequence-to-sequence language model to predict a sequence of dialog states for an input task. the sequence of dialog states comprise an assignment of values to slots for which the user has indicated a preference in dialog sequences. the methods include providing the trained sequence-to-sequence language model.
