Query-Answer Pair Generation via Topic Classification and Passage Extraction
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Solution Overview
Problem
Conventional search engines face limitations in providing instant answers to fact-seeking queries, as existing methods are labor-intensive and only capable of handling a small subset of popular queries, failing to address queries outside of predefined categories.
Innovation Solution
A platform that includes a topic-specific query classifier, domain identifier, and passage extraction system to generate query-answer pairs for various topics, utilizing user feedback and hierarchical rankers to identify relevant domains and passages, enabling the platform to handle a wide range of fact-seeking queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and table-based approaches are used to identify instant answers, then accuracy for popular queries is improved, but the system can only handle a small subset of queries and requires significant labor
Solution Approach 1:
The system employs crowd-sourced workers to automatically generate and validate query-answer pairs without requiring manual expert review for each query. The crowd-sourcing platform enables the system to self-expand its knowledge base by leveraging the collective intelligence of numerous users who contribute answers and validate query classifications
Solution Approach 2:
The system pre-processes and classifies queries using trained classifiers before routing them to appropriate answer sources. By performing preliminary classification of fact-seeking queries versus other query types, the system prepares the query base in advance, enabling efficient matching with pre-collected answer data from multiple sources
2Ease of manufacture
If predefined topic categories are used to organize answers, then management is simplified, but the system fails to address queries outside of these predefined categories
Solution Approach 1:
The system implements a universal query classification framework that can identify fact-seeking queries across any topic domain, not limited to predefined categories. The classification system and answer matching mechanism are designed to work universally across diverse topics by using general-purpose classifiers and multiple answer sources that cover broad subject areas
Solution Approach 2:
The system dynamically adapts to new topics and query types by continuously training classifiers on new data and incorporating answers from emerging sources. Rather than being static with fixed categories, the system evolves its knowledge base and classification capabilities to handle previously unseen topics and query patterns
3Measurement precision
If labor-intensive manual approaches are used to identify answers, then answer quality is improved, but productivity and scalability are severely limited
Solution Approach 1:
The system uses crowd-sourced workers to automatically generate and validate answers at scale without requiring professional human reviewers for each query. This self-service approach enables thousands of queries to be processed simultaneously while maintaining quality through distributed validation by multiple independent workers
Solution Approach 2:
The answer identification process is segmented into multiple independent stages: query classification, answer candidate generation from multiple sources, crowd-sourced validation, and quality filtering. This segmentation allows parallel processing of different query batches while maintaining quality control at each stage through specialized sub-processes
Data Source
AI summary
A platform for generating query-answer pairs is described herein, wherein the platform is well-suited for generating query-answer pairs for a variety of topics. The platform comprises a topic-specific classifier that is configured to identify queries from a query log that are fact-seeking queries that belong to a topic. The platform further comprises a domain identifier component that identifies domains that are relevant to the topic, wherein the domains comprise pages. The platform further includes a passage extraction system that comprises a hierarchy of rankers, wherein the passage extraction system is configured to identify passages in the pages that belong to the domain that comprise answers to the fact-seeking queries that belong to the topic.