A selective reading comprehension method and device based on the Dot Triple-Attention mechanism

By implementing the Dot Triple-Attention mechanism to achieve direct interaction between the text, questions, and options, the problem of insufficient sequential interaction in existing methods is solved, thereby improving the accuracy and efficiency of selective reading comprehension.

CN115345172BActive Publication Date: 2026-07-03QILU UNIVERSITY OF TECHNOLOGY (SHANDONG ACADEMY OF SCIENCES)
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Patent Information

Application Number
CN202210975775.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-08-15
Publication Date
2026-07-03
Estimated Expiration
2042-08-15

AI Technical Summary

Technical Problem

Existing selective reading comprehension methods cannot effectively realize the direct interaction between the three text sequences of article, question, and option, resulting in insufficient model prediction accuracy.

Method used

A selection-based reading comprehension method based on the Dot Triple-Attention mechanism is adopted. By constructing a selection-based reading comprehension model, a pre-trained embedding representation module, a feature filtering module, a Dot Triple-Attention interaction module, and a feature aggregation module are used to realize direct interaction between three text sequences: article, question, and option.

Benefits of technology

It improves the prediction accuracy of selective reading comprehension systems by capturing rich semantic features and efficient sequence interactions, thereby enhancing the computational and training efficiency of the model and reducing training costs.

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Abstract

This invention discloses a selection-based reading comprehension method, apparatus, storage medium, and electronic device based on the Dot Triple-Attention mechanism, belonging to the fields of natural language processing and artificial intelligence. The technical problem this invention aims to solve is how to more efficiently utilize text information to promote interaction between questions and options, and how to achieve direct interaction between the three sequences, thereby improving the prediction accuracy of the selection-based reading comprehension system. The technical solutions adopted are: ① A selection-based reading comprehension method based on the Dot Triple-Attention mechanism, comprising the following modules: a pre-trained embedding representation module, a feature filtering module, a Dot Triple-Attention interaction module, a feature aggregation module, and a label prediction module. ② A selection-based reading comprehension apparatus based on the Dot Triple-Attention mechanism, comprising: a pre-trained embedding representation module unit, a feature filtering module unit, a Dot Triple-Attention interaction module unit, a feature aggregation module unit, and a label prediction module unit.
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