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16 August 2023 BAMGAN: a KBQA method based GAN and bi-directional attention
Jiazhi Guo
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Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127872B (2023) https://doi.org/10.1117/12.3004671
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China
Abstract
Knowledge-based question answering(KBQA) involves using knowledge base technology to generate answers to natural language processing(NLP) questions. KBQA is one of the most challenging tasks in the field of NLP. Answer selection plays a vital role in KBQA, as it requires selecting the correct answer from a pool of candidate answers. However, knowledge bases still struggle to identify the correct answer from multiple candidate answers. To address this issue, In this paper, we propose BAMGAN, a KBQA method based on generative adversarial networks. This method uses generative adversarial networks (GANs) to improve answer selection accuracy in deep neural networks. Experimental results show the effectiveness of the BAMGAN.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jiazhi Guo "BAMGAN: a KBQA method based GAN and bi-directional attention", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127872B (16 August 2023); https://doi.org/10.1117/12.3004671
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KEYWORDS
Feature extraction
Deep learning
Adversarial training
Neural networks
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