Dialogue Processing Query Extraction and Knowledge Retrieval
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Solution Overview
Problem
Current Task-Oriented Dialogue (TOD) systems face challenges in efficiently processing dialogues due to heavy computational burdens and poor adaptability to different fields, particularly in end-to-end trainable systems that require continuous model updates and long input sequences, and streamline systems that rely heavily on predefined dialogue schemas.
Innovation Solution
A method for processing dialogues that involves obtaining dialogue text, extracting a current query text, determining the relevant knowledge database field, querying the database for knowledge results, and generating responses without encoding the entire database, allowing for decoupling of knowledge retrieval and response generation, thereby improving adaptability across different fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entire knowledge database is encoded and input into the end-to-end TOD system, then the system can access comprehensive knowledge, but the computational burden increases significantly and the input sequence becomes excessively long
Solution Approach 1:
The patent segments the knowledge database into individual knowledge points with unique identifiers, rather than encoding the entire database as a single large sequence. This allows the system to retrieve specific knowledge points independently, reducing the effective input sequence length and computational burden while maintaining access to comprehensive knowledge through the knowledge base.
Solution Approach 2:
The patent extracts only the necessary knowledge points related to the current dialogue context from the large knowledge database, rather than processing the entire database. This extraction is achieved through efficient retrieval mechanisms that identify and fetch only relevant knowledge points, reducing computational load while preserving information completeness.
2Device complexity
If the TOD system uses predefined dialogue schemas, then the system structure is well-defined, but the adaptability to different fields deteriorates
Solution Approach 1:
The patent implements a universal dialogue processing framework that can handle multiple fields and domains through the same core architecture. The system uses field-agnostic components such as the dialogue state tracker and response generator, combined with flexible knowledge point retrieval, enabling it to adapt to different fields without requiring field-specific model training or complex schema definitions for each domain.
Solution Approach 2:
The patent introduces dynamic adaptability by allowing the system to flexibly adjust to different fields through configurable knowledge bases and adaptive retrieval strategies. The dialogue state tracking and response generation mechanisms can dynamically adapt to different domain requirements without fixing the system structure to predefined schemas, enabling versatile field adaptation.
3Reliability
If the end-to-end TOD system continuously updates model parameters, then the system learns from new data, but the training time and computational resources increase
Solution Approach 1:
The patent extracts and utilizes external knowledge from the knowledge database to supplement model training, rather than relying solely on continuous parameter updates from dialogue data. This knowledge extraction approach allows the system to incorporate new information without requiring extensive retraining, reducing training time while maintaining learning capability through knowledge base updates.
4Ease of operation
If the streamline TOD system uses multiple modules processed in sequence, then the system is interpretable, but the processing efficiency decreases
Solution Approach 1:
The patent merges the dialogue state tracking and response generation into a more integrated framework that maintains interpretability while improving efficiency. The system combines modular components with optimized information flow, allowing sequential processing benefits for interpretability while reducing redundant computations to enhance processing efficiency.
Data Source
AI summary
A method for processing a dialogue includes: obtaining a dialogue text of the dialogue, in which the dialogue text includes a current question text, or the dialogue text includes the current question text and a historical dialogue text; extracting a current query text from the dialogue text; obtaining a knowledge query result for the current query text by querying a knowledge database based on the current query text; and determining a response text for the current question text based on the knowledge query result and the dialogue text.