Dialogue Generation Adjusting Positive Negative Sentence Proportion
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Solution Overview
Problem
Conventional conversation systems struggle to create clear and understandable responses, as positive and negative will feeling words are irregularly mixed, making it difficult for users to comprehend the content, especially in communication devices using agent functions.
Innovation Solution
An information processing system and method that detect a user's proficiency level with respect to an agent device and generate dialogue sentence data by adjusting the proportion of positive and negative sentences based on the user's proficiency level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If positive and negative will feeling words are irregularly mixed in response sentences, then the agent can demonstrate various dialogue expressions, but the user finds it difficult to understand the content
Solution Approach 1:
The system dynamically adjusts the proportion of positive and negative sentences based on real-time detection of user proficiency level. When proficiency is low, the system increases positive sentences to improve understanding. When proficiency is high, the system increases negative sentences to enhance dialogue variety. This dynamic adaptation resolves the contradiction by making the system responsive to user state changes.
Solution Approach 2:
The system changes the parameter of sentence proportion (positive vs. negative) according to the detected user proficiency level. By adjusting this parameter dynamically, the system optimizes both understandability and expression variety at different stages of user interaction, resolving the contradiction between these two objectives.
2Ease of operation
If only positive sentences are used, then the content is easy to understand, but the dialogue expression becomes limited
Solution Approach 1:
The system dynamically transitions from using primarily positive sentences when user proficiency is low to using a more balanced or negative-sentence-heavy approach when proficiency is high. This dynamic strategy ensures that expression variety is maintained without compromising understanding at any stage of user interaction.
Solution Approach 2:
The system preliminarily uses positive sentences to establish clear communication when the user is less proficient, then gradually introduces negative sentences as user proficiency increases. This staged approach ensures understanding is established before introducing complexity, resolving the contradiction between ease of understanding and expression variety.
3Ease of operation
If negative sentences are not used at all, then the content is easy to understand, but the agent's function is not demonstrated
Solution Approach 1:
The system dynamically introduces negative sentences into the dialogue based on the detected user proficiency level. When proficiency is high, the system incorporates negative sentences to demonstrate full agent functionality. When proficiency is low, the system maintains positive sentences to ensure understanding, thus resolving the contradiction between functionality and understandability.
Solution Approach 2:
The system uses feedback from user proficiency detection to adjust the sentence composition. The detected proficiency level serves as feedback that triggers appropriate adjustment in sentence usage, ensuring that negative sentences are introduced only when the user can handle them, thus maintaining both functionality and understandability.
Data Source
AI summary
An information processing system includes an agent device and an information processing device. The agent device has an agent function. The information processing device generates dialogue sentence data for a user. The information processing system outputs the generated dialogue sentence data to the user using the agent function. The information processing device includes: a load estimation unit that estimates a load when the user recognizes the dialogue sentence data; and a data generation unit that generates the dialogue sentence data using response sentence information classified into positive sentences and negative sentences. When the load of the user is relatively high, the data generation unit increases a proportion of the positive sentences used in the dialogue sentence data as compared to when the load of the user is relatively low.