Machine Learning Question Augmentation for Complete Document Retrieval
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Solution Overview
Problem
Existing question-answer systems face challenges in efficiently processing user queries due to the large number of search results and the need for users to sift through irrelevant or semantically unimportant content, especially when queries are overly terse or verbose, leading to suboptimal search performance.
Innovation Solution
The system modifies training questions to expand terse queries and simplify verbose ones, tags contextual information for improved content filtering, cleanses and normalizes content, and adjusts operations based on computing resources to enhance search quality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system processes all search results to ensure comprehensive coverage, then the completeness of information is improved, but the time and computational resources required increase significantly
Solution Approach 1:
The system performs preliminary actions by generating multiple augmented questions from a single source question before conducting the search. This preprocessing step creates a more comprehensive search strategy upfront, allowing the system to retrieve more complete information in a single search operation rather than requiring multiple sequential searches, thus reducing total search time while maintaining information completeness.
Solution Approach 2:
The system creates copies of the source question through augmentation, generating multiple variant questions that explore different semantic angles. These copied and modified questions are then used to search the document collection, ensuring that the search covers various interpretations and formulations of the original query, thereby improving information completeness without requiring users to manually reformulate their search terms.
2Measurement precision
If the system uses detailed and specific training questions, then the precision of answer matching is improved, but the difficulty of processing and the computational complexity increase
Solution Approach 1:
The system segments the complex task of answering a source question into multiple simpler sub-tasks by generating several augmented questions. Each augmented question focuses on a specific aspect or formulation of the original query, making individual processing easier while collectively covering the full semantic space. This segmentation reduces the complexity of processing each individual question while maintaining high matching precision through the combined results.
Solution Approach 2:
The question augmentation module serves multiple functions: it expands terse questions, simplifies verbose questions, generates semantic variants, and creates targeted search queries. This multi-functional approach allows the system to handle diverse question types and complexities through a single unified process, reducing overall processing complexity while maintaining precision across different query styles.
3Reliability
If the system expands terse questions to include more details, then the quality of search results is improved, but the time required for question processing increases
Solution Approach 1:
The system performs question expansion as a preliminary action before the search process. By automatically generating augmented questions with appropriate details and semantic variations upfront, the system eliminates the need for iterative question refinement during search execution. This preliminary expansion improves search result quality while keeping the additional processing time minimal and confined to the pre-search phase.
4Measurement precision
If the system processes and analyzes all source documents thoroughly, then the accuracy of information retrieval is improved, but the computational resources and energy consumption increase
Solution Approach 1:
The system performs preliminary question augmentation to generate multiple targeted search queries before processing documents. This preliminary step creates a more efficient search strategy that guides the document processing toward relevant information, reducing the need to thoroughly analyze every document in the collection. The augmented questions act as filters that focus computational energy on the most promising document sections.
Solution Approach 2:
The system segments the document processing task by using augmented questions to identify and focus on specific relevant portions of documents. Rather than processing all documents uniformly, the system uses the multiple question variants to guide selective processing, dividing the large task into smaller, more manageable segments that require less computational energy while maintaining retrieval accuracy.
Data Source
AI summary
Disclosed are methods, systems, devices, apparatus, media, and other implementations that include a method for document processing (particularly for training of a machine learning question answering platform, and for ingestion of documents). The method includes obtaining a question dataset (e.g., either from public or private repositories of questions) comprising one or more source questions for document processing by a machine learning question-and-answer system that provides answer data in response to question data submitted by a user, modifying a source question from the question dataset to generate one or more augmented questions with equivalent semantic meanings as that of the source question, and processing a document with the one or more augmented questions.