Communicative Discourse Trees for Detecting Distributed Incompetence
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Solution Overview
Problem
Current systems for autonomous agents, such as chatbots, face challenges in accurately detecting distributed incompetence in text-based interactions, leading to inconsistent and unsatisfactory responses to user queries.
Innovation Solution
The use of communicative discourse trees, combined with machine-learning techniques, to analyze rhetorical relationships and thematic roles in text, enabling the detection of distributed incompetence and generation of more accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text analysis methods are used to detect distributed incompetence, then the system complexity is low, but the detection accuracy and reliability are insufficient
Solution Approach 1:
The text analysis system is segmented into multiple hierarchical levels: surface linguistic features, discourse structures, and pragmatic inferences. Each level processes specific aspects of the text independently, then combines results to achieve comprehensive detection of distributed incompetence with high accuracy while managing system complexity through modular design
Solution Approach 2:
Discourse trees serve as an intermediary data structure that bridges raw text and incompetence detection algorithms. The discourse tree captures rhetorical relationships and information flow patterns, enabling accurate detection without requiring direct complex analysis of the original text, thus improving precision while maintaining manageable system complexity
2Measurement precision
If simple response generation is used, then the response time is fast, but the response accuracy and appropriateness are insufficient
Solution Approach 1:
The system performs preliminary analysis of discourse structures and identifies potential incompetence patterns before generating responses. By pre-processing text into discourse trees and pre-identifying rhetorical relationship anomalies, the system prepares detection results in advance, enabling accurate response generation without excessive processing time
Solution Approach 2:
The system replaces traditional rule-based response generation with machine learning models that have been trained on discourse patterns. These models automatically learn appropriate response strategies based on detected incompetence types, improving response accuracy and appropriateness while maintaining efficient processing speeds through optimized neural network inference
Data Source
AI summary
Techniques are disclosed for detecting distributed incompetence in text of a conversation using communicative discourse trees and then inserting an automatic response from an autonomous agent (chatbot) or other entity. For example, a computing system generates a communicative discourse tree from utterances from multiple agents to a user. The computing system obtains a prediction of whether the text includes distributed incompetence by applying a trained predictive model to the communicative discourse tree. Based on the detection, the computing system generates an updated response to a user device.