Generating Long Financial Report Using Conditional Variational Autoencoders With Knowledge Distillation

Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a conditional variational autoencoders (CVAEs)-based approach that distills external...

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Veröffentlicht in:IEEE transactions on artificial intelligence Jg. 5; H. 4; S. 1669 - 1680
Hauptverfasser: Wang, Ziao, Ren, Yunpeng, Zhang, Xiaofeng, Wang, Yiyuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2024
Schlagworte:
Biological system modeling
Conditional variational autoencoder (CVAE)
Decoding
financial report generation
Generative adversarial networks
Generators
knowledge distillation (KD)
Knowledge engineering
Logic gates
natural language generation (NLG)
Task analysis
ISSN:2691-4581, 2691-4581
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Zusammenfassung:Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a conditional variational autoencoders (CVAEs)-based approach that distills external knowledge from a set of news-report data. Specifically, we design an encoder-decoder architecture to learn the latent variable distribution from this set of news-report data to provide background knowledge. Next, a teacher-student network is employed to distill knowledge to refine the output of the decoder component. To evaluate the model performance, extensive experiments have been performed on two public datasets using evaluation criteria like BLEU, ROUGE, METEOR, and human evaluation. Our promising experimental results demonstrate that our proposed approach outperforms existing state-of-the-art approaches.
ISSN:2691-4581
2691-4581
DOI:10.1109/TAI.2024.3351594