QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input
Le Zhang, Rongsheng Zhang, Xiaoxi Mao, Yongzhu Chang · 2022
Lyrics generation has been a very popular application of natural language generation.Previous works mainly focused on generating lyrics based on a couple of attributes or keywords, rendering very limited control over the content of the lyrics.In this paper, we demonstrate the QiuNiu, a Chinese lyrics generation system which is conditioned on passage-level text rather than a few attributes or keywords.By using the passage-level text as input, the content of generated lyrics is expected to reflect the nuances of users' needs.The QiuNiu system supports various forms of passage-level input, such as short stories, essays, poetry.The training of it is conducted under the framework of unsupervised machine translation, due to the lack of aligned passage-level text-to-lyrics corpus.We initialize the parameters of Qiu-Niu with a custom pretrained Chinese GPT-2 model and adopt a two-step process to finetune the model for better alignment between passage-level text and lyrics.Additionally, a postprocess module is used to filter and rerank the generated lyrics to select the ones of highest quality.The demo video of the system is available at https://youtu.be/OCQNzahqWgM.