| Issue |
ITM Web Conf.
Volume 73, 2025
International Workshop on Advanced Applications of Deep Learning in Image Processing (IWADI 2024)
|
|
|---|---|---|
| Article Number | 02012 | |
| Number of page(s) | 8 | |
| Section | Machine Learning, Deep Learning, and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257302012 | |
| Published online | 17 February 2025 | |
Controllable text generation based on varied frameworks - rhyming lyrics generation technology
1 School of Computer Science and Technology, Guangdong University of Technology, Guangdong, 510000, China
2 School of Computer Science, Wuhan University, Hubei, 430072, China
3 School of Architecture, South China University of Technology, Guangdong, 510006, China
* Corresponding author: 2023302111273@whu.edu.cn
Controllable Text Generation, as a cutting-edge technology in Natural Language Processing (NLP), has significantly improved the quality of text generation. Users can customize the generated content by setting specific attributes, formats, and emotional characteristics, thereby achieving the goal of conserving resources. However, despite notable progress in this field, several challenges remain, such as limited text diversity under multiple conditions and information disconnection during long-text generation. In light of this, this paper focuses on controllable text generation technology within a Chinese context, particularly emphasizing the key element of rhyming. The aim is to investigate an effective method for generating rhyming lyrics and poetry. By comparing the text generation performance under Recurrent Neural Networks (RNN), Bidirectional Recurrent Neural Networks (Bi-RNN), and Transformer frameworks, and evaluating the results using n-grams metrics, this study attempts to reveal which architecture is better suited for handling the specific controllable generation requirement of rhyming. This provides theoretical support and technical guidance for automatically creating Chinese poetry and lyrics.
© The Authors, published by EDP Sciences, 2025
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