Question Training Data Extension via Word Replacement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current question answering systems require a large number of pre-prepared question sentences to achieve high precision in predicting topics, necessitating continuous maintenance of dictionaries and grammars, and often need to prepare many pairs of questions and answers, which is inefficient.
Innovation Solution
An information processing apparatus that selects words in question training data and replaces them with corresponding words from answer data to automatically generate new question sentences, reducing the number of pre-prepared question sentences needed while enhancing topic prediction precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of pre-prepared question sentences are used, then topic prediction precision is improved, but system complexity and maintenance burden increase
Solution Approach 1:
The system performs preliminary actions by automatically generating question sentences from existing answer data before actual topic prediction tasks. The question sentence generation unit creates multiple variations of questions by replacing words in answer data with synonyms or related terms, preparing a comprehensive question database in advance without manual intervention.
Solution Approach 2:
The system serves itself by automatically generating question sentences using its own answer data and the question sentence generation unit. This self-service mechanism eliminates the need for external manual creation and maintenance of question databases, reducing system complexity while maintaining precision.
2Measurement precision
If many pairs of questions and answers are prepared manually, then topic prediction precision is improved, but time consumption and labor cost increase
Solution Approach 1:
The system automatically generates question sentences using the question sentence generation unit, which processes answer data to create multiple question variations. This self-service approach eliminates manual preparation time while ensuring sufficient question coverage for accurate topic prediction.
Solution Approach 2:
The system performs preliminary question generation automatically before deployment. The generation unit creates question sentences by substituting words in answer data with alternatives from a thesaurus or synonym database, preparing comprehensive training data in advance without consuming manual labor time.
3Reliability
If dictionaries and grammars are continuously maintained, then language accuracy is improved, but maintenance effort and time increase
Solution Approach 1:
The system maintains language accuracy through self-service mechanisms where the question sentence generation unit automatically updates question databases using existing answer data and built-in thesaurus resources. This eliminates the need for continuous manual dictionary and grammar maintenance while preserving language precision.
Data Source
AI summary
An information processing apparatus includes a selector and an extending unit. The selector selects a word in question training data corresponding to a topic. The extending unit extends the question training data by replacing the word selected by the selector in the question training data by a word in answer data corresponding to the topic.