18148037. Demonstration-driven Scalable Task-oriented Dialogue Modeling simplified abstract (Google LLC)
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 18148037 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 related to a task.
- Creating contextual representations from a history of exchanged utterances.
- Training a sequence-to-sequence language model to predict dialog states.
- Assigning 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.
Problems Solved: This technology addresses the challenge of accurately predicting user preferences and dialog states in conversational interactions.
Benefits:
- Improved accuracy in understanding user preferences.
- Enhanced efficiency in providing relevant responses.
- Better user experience in interacting with AI systems.
Commercial Applications: The technology can be used in customer service industries to automate responses, streamline interactions, and improve overall customer satisfaction.
Prior Art: Prior research in natural language processing and dialog systems may provide insights into similar approaches to predicting dialog states in conversations.
Frequently Updated Research: Stay updated on advancements in natural language processing, machine learning, and dialog systems to enhance the performance of this technology.
Questions about Dialog State Prediction: 1. How does this technology improve the accuracy of predicting dialog states in conversations? 2. What are the potential limitations of using a sequence-to-sequence language model for dialog state prediction?
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.
