Automated QA Pair Discovery from Conversation Data
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Solution Overview
Problem
Current computer systems struggle to efficiently discover new question and answer knowledge from conversations, as they often rely on pre-defined QA pairs and lack the ability to automatically refine and adapt to changing conversation patterns.
Innovation Solution
A system and method that utilize a processor to receive a question and answer pair, search a corpus of conversations for matching segments, tag statements with dialog labels, and compute the effectiveness of question and answer structure patterns to select and transform them into new QA pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system relies on pre-defined QA pairs, then the initial accuracy is maintained, but the adaptability to changing conversation patterns deteriorates
Solution Approach 1:
The system implements feedback by searching conversation corpora for segments containing generated QA pairs, computing effectiveness scores based on labeled data, and using these scores to refine future QA pair generation. This continuous feedback loop enables the system to adapt to changing conversation patterns while maintaining accuracy through empirical validation.
Solution Approach 2:
The system performs self-service by automatically discovering new QA pairs from conversation data without requiring manual annotation for training. The processor autonomously extracts QA pairs, tags conversation segments with dialog labels, computes effectiveness, and refines patterns, eliminating the need for continuous human intervention in data preparation.
2Measurement precision
If the system manually annotates conversation data, then the precision of QA patterns is improved, but the time required for processing deteriorates
Solution Approach 1:
The system automatically performs the annotation task by having the processor tag conversation segments with dialog labels based on the extracted QA pairs and conversation context. This self-annotation capability maintains precision comparable to manual annotation while dramatically reducing processing time by eliminating human labor requirements.
Solution Approach 2:
The system changes the parameter of annotation methodology from manual human labeling to automated computational labeling. By transforming the annotation process into an algorithmic operation that processes entire conversation corpora efficiently, the system achieves both high precision and reduced time requirements.
3Productivity
If the system uses automated pattern discovery, then the productivity of QA pair generation is improved, but the complexity of the processing system deteriorates
Solution Approach 1:
The system segments the complex QA pair generation process into distinct manageable stages: extracting QA pairs from conversations, tagging conversation segments with dialog labels, searching for matching patterns, computing effectiveness scores, and refining final QA pairs. This segmentation reduces overall system complexity by breaking down the complex task into simpler sequential operations.
Solution Approach 2:
The processor performs multiple functions within a single integrated system: it extracts QA pairs, tags conversation segments, searches conversation corpora, computes effectiveness metrics, and refines patterns. This multi-functionality reduces the need for separate specialized systems, thereby improving productivity while controlling overall complexity through consolidation.
Data Source
AI summary
New question and answer (QA) pairs can be automatically discovered from a corpus of data such as online chats and conversations. Newly discovered QA pairs can augment QA database, which can be used by a computer processor or device, e.g., by a chatbot, an automated machine, and/or another. Existing QA knowledge can be used to learn the structures of QA knowledge distribution in conversations, and new QA knowledge can be automatically learned through the structure of learned QA knowledge distribution in conversations. The structure of learned QA knowledge distribution can be refined by adding more semantics based on labeled data.