Automated IR Evaluation via Synthetic Natural Language Queries
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Solution Overview
Problem
Existing evaluation methods for information retrieval (IR) systems are either time-consuming and subjective (user-based evaluation) or lack natural language queries (system evaluation), necessitating a more automated and effective method to compare IR systems using auto-generated natural language queries.
Innovation Solution
A computer-implemented method that utilizes a pre-indexed knowledge-based document and trained sentence identification and query generation models to automatically generate queries, which are then used to evaluate IR systems by ranking query-worthy sentences and generating queries based on prediction probabilities and features like location and entities within the document.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user-based evaluation is used to assess IR systems, then the evaluation focuses on overall outcome and user satisfaction, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent creates synthetic query-document pairs by copying and adapting existing document content to generate test queries automatically. This allows system evaluation to mimic user-based evaluation scenarios without requiring actual user participation, thus maintaining evaluation accuracy while eliminating time and cost overhead
Solution Approach 2:
The evaluation system performs self-assessment by automatically generating queries from the indexed documents themselves and evaluating how well the IR system retrieves them. This self-service mechanism eliminates dependency on external human evaluators while maintaining objective measurement of system performance
2Productivity
If system evaluation with manually created test queries is used, then the evaluation is more automated and less expensive, but the test queries are not in natural language and differ from real user queries
Solution Approach 1:
The patent replaces manual query creation (mechanical human process) with an automated natural language generation system. The system uses template-based generation and statistical methods to create queries that mimic natural user language while maintaining full automation, thus substituting the mechanical process of manual query writing with an automated linguistic generation process
Solution Approach 2:
The patent changes the parameters of query generation by using multiple templates with varying structures and styles, adjusting formality levels, and modifying query complexity. This allows the automated system to generate diverse natural language queries that better reflect real user behavior while maintaining automation and scalability
3Extent of automation
If formal language queries are used for system evaluation, then automation is improved, but the queries differ from natural language queries that users actually write
Solution Approach 1:
The patent creates a universal query generation framework that can produce multiple types of queries (simple keyword queries, natural language questions, complex multi-concept queries) from the same document corpus. This multi-functional system maintains high automation while adapting to generate various query styles that reflect different user information needs and search behaviors
Data Source
AI summary
Embodiments provide a computer implemented method of evaluating one or more IR systems, the method including: providing, by a processor, a pre-indexed knowledge-based document to a pre-trained sentence identification model; identifying, by the sentence identification model, a predetermined number of query-worthy sentences from the pre-indexed knowledge-based document, wherein the query-worthy sentences are ranked based on a prediction probability value of each query-worthy sentence; providing, by the sentence identification model, the query-worthy sentences to a pre-trained query generation model; generating, by the query generation model, a query for each query-worthy sentence; and evaluating, by the processor, the one or more IR systems using the generated queries, wherein one or more searches are performed via the one or more IR systems, and the one or more searches are performed in a set of knowledge-based documents including the pre-indexed knowledge-based document.