Paper
19 July 2024 Text implication recognition with multiround and multi-interaction inference mechanism based on deep learning
Jinyu Zhu, Huimin Zhao, Wu Deng
Author Affiliations +
Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810H (2024) https://doi.org/10.1117/12.3031360
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China
Abstract
As a basic subtask in natural language processing, text enunciation recognition aims at determining the enunciation relationship between presupposition and hypothesis. At present, most deep learning text sensitivity models use single interaction and single inference to process the semantic features of the text, which is not sufficient and comprehensive for semantic mining of Chinese text. Therefore, a multi-round and multi-interaction inference model for Chinese text implication (MM-CIM) is developed in this paper. On the one hand, different mutual attention mechanisms are used for the premise and hypothesis to generate different interactive alignment semantic features to expand the model's exploration of text semantics. On the other hand, the coding features and mixed features of the premise and hypothesis are deduced in turn to deepen the model's exploration of text semantics. In addition, k-max pooling is introduced into the pooling part of the model to further expand the semantic mining ability of the model. The experimental results on Chinese text contained data show that all parts of the proposed model are optimized effectively and have better recognition effect than the basic model.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jinyu Zhu, Huimin Zhao, and Wu Deng "Text implication recognition with multiround and multi-interaction inference mechanism based on deep learning", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810H (19 July 2024); https://doi.org/10.1117/12.3031360
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KEYWORDS
Semantics

Education and training

Data modeling

Matrices

Deep learning

Performance modeling

Error analysis

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