Article: Construction of cross-media semantic relevance model for college English teaching based on output-driven hypothesis and two-way attention feature learning Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.93 pp.1 - 20 Abstract: With the wide application of multimedia technology in English teaching, how to effectively correlate the semantic information of cross-media data (such as text, audio and images) has become a key challenge. This paper aims to construct a cross-media semantic association model for college English teaching (ODBA-CMSA) based on output-driven hypothesis and bidirectional attention feature learning to improve the accuracy and interpretability of grading. The model optimises semantic representation through multi-modal embedding, bi-directional attention mechanism, and output-driven loss. The test results show that the model is significantly better than the baseline in accuracy rate (89.5%) and F1-score (89.0%), and its robustness (accuracy rate only decreases by 5.1% under noise) and practicability (user satisfaction 4.4 points) are outstanding, which verifies its effectiveness in complex teaching environment. This study not only provides an efficient semantic association solution, but also enhances the transparency of the model through interpretability design, which provides a new idea for the development of intelligent teaching system. Inderscience Publishers - linking academia, business and industry through research

Title: Construction of cross-media semantic relevance model for college English teaching based on output-driven hypothesis and two-way attention feature learning

Authors: Liping Deng

Addresses: The School of Foreign Languages, Nanchang Institute of Technology, 330044, Jiangxi, China

Abstract: With the wide application of multimedia technology in English teaching, how to effectively correlate the semantic information of cross-media data (such as text, audio and images) has become a key challenge. This paper aims to construct a cross-media semantic association model for college English teaching (ODBA-CMSA) based on output-driven hypothesis and bidirectional attention feature learning to improve the accuracy and interpretability of grading. The model optimises semantic representation through multi-modal embedding, bi-directional attention mechanism, and output-driven loss. The test results show that the model is significantly better than the baseline in accuracy rate (89.5%) and F1-score (89.0%), and its robustness (accuracy rate only decreases by 5.1% under noise) and practicability (user satisfaction 4.4 points) are outstanding, which verifies its effectiveness in complex teaching environment. This study not only provides an efficient semantic association solution, but also enhances the transparency of the model through interpretability design, which provides a new idea for the development of intelligent teaching system.

Keywords: output drive; two-way attention; cross-media; college English; semantic association.

DOI: 10.1504/IJICT.2026.155873

International Journal of Information and Communication Technology, 2026 Vol.27 No.93, pp.1 - 20

Received: 07 Jan 2026
Accepted: 05 Mar 2026
Published online: 24 Aug 2026 *

Title: Construction of cross-media semantic relevance model for college English teaching based on output-driven hypothesis and two-way attention feature learning

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