Idiom Fill-in-the-Blank Answer Selection via Confidence Sum
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Solution Overview
Problem
Existing methods for answering idiom fill-in-the-blank questions are inefficient due to the need for manual collection of incomplete question data, which prevents automatic intelligent judgment and makes it difficult for parents or students to find accurate answers.
Innovation Solution
A method and apparatus that utilize a pre-trained idiom selection fill-in-the-blank model, incorporating a KM algorithm, to calculate confidence scores for candidate idioms filling in blanks, randomly arranging idioms to form groups of answers, and selecting the group with the highest confidence sum as the target answer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual collection of idiom fill-in-the-blank questions is used, then answers can be obtained through human intelligence, but the collected questions are incomplete and cannot be automatically judged
Solution Approach 1:
The patent replaces manual human intelligence with an automated deep learning model (BERT-based idiom selection fill-in-the-blank model) to perform idiom selection. The model automatically processes fill-in-the-blank texts and candidate idioms, generating confidence scores without requiring human intervention, thereby achieving both high accuracy and full automation.
Solution Approach 2:
The system enables self-service by allowing the deep learning model to independently complete the entire idiom selection process. The model automatically inputs text, evaluates candidate idioms, calculates confidence scores, and outputs results without needing human collection or judgment, making the system self-sufficient and fully automated.
2Loss of time
If existing APPs and Internet search methods are used, then answers can be found through manual search, but the process is time-consuming and answers cannot be automatically judged
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning model on extensive idiom data before deployment. The BERT model is pre-trained with idiom knowledge and selection patterns, enabling it to rapidly and accurately answer new questions without requiring time-consuming manual search or collection during actual use.
Solution Approach 2:
The patent replaces manual search processes with an automated deep learning system that instantly processes queries. The model automatically generates answers by evaluating candidate idioms against the fill-in-the-blank text, eliminating the need for time-consuming human search through APPs or the Internet while dramatically improving productivity.
3Reliability
If manually collected questions are used, then some answers can be obtained, but the questions are incomplete making it difficult to find accurate answers
Solution Approach 1:
The patent applies universality by designing a deep learning model that can handle diverse idiom fill-in-the-blank questions of varying types and difficulties. The BERT-based model is trained on comprehensive data and can process different question formats, making the system universally applicable to complete idiom selection tasks regardless of question specifics, thereby improving reliability with complete data coverage.
Data Source
AI summary
Disclosed are a method and apparatus for selecting answers to idiom fill-in-the-blank questions, a computer device, and a storage medium. The method includes: obtaining a question text of idiom fill-in-the-blank questions, the question text including a fill-in-the-blank text and n candidate idioms, and the fill-in-the-blank text including m fill-in-the-blanks to be filled in with the candidate idioms; obtaining an explanatory text of all the candidate idioms; obtaining, through an idiom selection fill-in-the-blank model, a confidence that each fill-in-the-blank is filled in with each candidate idiom; selecting m idioms from the n candidate idioms to form multiple groups of answers; calculating a sum of the confidences that the fill-in-the-blanks are filled in with the candidate idioms in each group of answers; and obtaining a group of answers with the highest confidence sum as answers to the idiom fill-in-the-blank questions. The present application implements answers to idiom fill-in-the-blank questions with high accuracy.