Discourse Tree Question Generation for Virtual Dialogue
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Solution Overview
Problem
Existing solutions for computer-implemented linguistics are unable to fully leverage textual content for generating questions and answers, limiting their effectiveness in applications such as autonomous agents and virtual dialogue systems.
Innovation Solution
The use of discourse analysis techniques, including rhetorical structure theory, communicative discourse trees, and template matching, to identify questions and answers within text and generate suitable question fragments, which are then verified and inserted into the text for improved dialogue construction and training data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing solutions for computer-implemented linguistics are used, then basic text processing is possible, but the ability to fully leverage textual content for generating questions and answers is limited
Solution Approach 1:
The patent segments text into elementary discourse units (EDUs) and further divides them into nucleus and satellite components. This segmentation enables the system to identify specific question-answer relationships within text by analyzing the rhetorical roles of different segments, thereby improving the ability to generate questions and answers from textual content.
Solution Approach 2:
The patent introduces discourse trees as an intermediary structure that represents rhetorical relationships between text segments. This intermediary representation allows the system to systematically analyze and generate questions and answers by traversing the discourse tree structure, bridging the gap between raw text and dialogue construction.
2Measurement precision
If discourse analysis techniques are applied to identify questions and answers, then the identification accuracy improves, but the processing complexity increases
Solution Approach 1:
By segmenting text into elementary discourse units and further into nucleus-satellite pairs, the system breaks down the complex task of question-answer identification into manageable sub-tasks. Each segment can be analyzed independently for its rhetorical role, improving identification accuracy while making the overall process more tractable.
Solution Approach 2:
The patent applies different analysis methods to different parts of the text based on their rhetorical roles. Nucleus segments are treated differently from satellite segments, with specific rules applied to each type. This localized approach allows for precise identification of questions and answers while avoiding unnecessary complexity in analyzing every text segment uniformly.
3Reliability
If question fragments are generated and inserted into text, then training data quality improves, but the time required for text annotation increases
Solution Approach 1:
The system performs preliminary discourse analysis and question fragment generation automatically before final annotation. By pre-identifying potential question-answer pairs and generating question fragments in advance, the system reduces the manual annotation time required while ensuring high training data quality through systematic rhetorical analysis.
Solution Approach 2:
The patent implements automated generation of question fragments from satellite elementary discourse units. The system serves itself by automatically creating annotated training data through discourse analysis, reducing reliance on manual annotation while maintaining high data quality through rule-based rhetorical relationship identification.
Data Source
AI summary
Systems, devices, and methods of the present disclosure use discourse analysis and other techniques to form questions and answers from text. The questions and answers can be used for different applications, including providing a virtual dialogue or generating training data for machine-learning models. For example, a dialogue application generates a discourse tree that represents text and identifies a question from a satellite elementary discourse unit of the discourse tree. The dialogue application annotates the text by inserting the generated question and labeling the satellite elementary discourse unit as an answer.