Dialogue Response Type Judgment for Accuracy and Flow
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Solution Overview
Problem
Current dialogue generation systems in AI technologies face challenges in generating accurate and efficient task-based responses, particularly in determining the type of response needed and ensuring smooth dialogue flow, which affects user experience.
Innovation Solution
A method and apparatus for generating dialogues that involve obtaining an input sentence, updating the dialogue state to determine the type of task-based response, and using a task-based dialogue response generator to produce a designated-type response, with additional processing for non-designated types by referencing historical dialogues to ensure coherence and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a task-based dialogue response generator is used to generate responses, then the accuracy of task-based responses is improved, but the dialogue flow smoothness deteriorates when responses are not of the designated type
Solution Approach 1:
The patent introduces a response type judgment module as an intermediary between the task-based dialogue response generator and the output. This module evaluates whether the generated response is of the designated type (task completion) or non-designated type (non-task completion). When a non-designated type response is generated, the system switches to a chat-based generator to maintain dialogue smoothness, thus resolving the contradiction between response accuracy and dialogue flow.
Solution Approach 2:
The system dynamically switches between task-based and chat-based dialogue response generators based on the response type judgment. This dynamic adaptation allows the system to prioritize task accuracy when appropriate while maintaining dialogue smoothness when needed, effectively resolving the contradiction between the two competing requirements.
2Reliability
If the system strictly enforces designated-type responses for task completion, then task completion accuracy is improved, but dialogue coherence deteriorates when initial responses are not of the designated type
Solution Approach 1:
The response type judgment module acts as an intermediary that evaluates generated responses and determines whether they are of designated or non-designated type. This intermediary layer allows the system to maintain task completion accuracy by identifying appropriate responses while preserving dialogue coherence by switching to chat-based generation when needed, thus resolving the contradiction between reliability and stability.
3Ease of operation
If multiple response generation paths are implemented to handle different response types, then dialogue quality is improved, but system complexity increases
Solution Approach 1:
The patent segments the dialogue response generation system into distinct modules: a task-based generator, a chat-based generator, and a response type judgment module. This segmentation allows each component to specialize in specific functions, improving overall dialogue quality while managing complexity through modular architecture where each segment handles a specific aspect of the generation process.
Data Source
AI summary
The disclosure provides a method for generating a dialogue. The method includes: obtaining an input sentence; determining a type of a task-based response sentence that is to be generated, by updating a current dialogue state based on the input sentence; generating the task-based response sentence by inputting the input sentence into a task-based dialogue response generator; and determining the task-based response sentence as a target response sentence in response to the type of the task-based response sentence being a designated type.