Generating lyrics and poems is one of the essential downstream tasks in the
Natural Language Processing (NLP) field. Current methods have performed well in
some lyrics generation scenarios but need further improvements in tasks
requiring fine control. We propose a novel method for generating ancient
Chinese lyrics (Song Ci), a type of ancient lyrics that involves precise
control of song structure. The system is equipped with a phrase retriever and a
phrase connector. Based on an input prompt, the phrase retriever picks phrases
from a database to construct a phrase pool. The phrase connector then selects a
series of phrases from the phrase pool that minimizes a multi-term loss
function that considers rhyme, song structure, and fluency. Experimental
results show that our method can generate high-quality ancient Chinese lyrics
while performing well on topic and song structure control. We also expect our
approach to be generalized to other lyrics-generating tasks.

提出了一种新方法用于生成中国古代词，该方法结合了短语检索和短语拼接，通过多项损失函数优化实现了对韵律，歌曲结构和流畅性的精确控制。实验结果表明，该方法可以高质量地生成中国古代词，而且在主题和歌曲结构控制方面表现出色。

基于词组原型检索的可控古文歌词生成

Controllable Ancient Chinese Lyrics Generation Based on Phrase Prototype Retrieving

In this paper, we propose a joint architecture that captures language, rhyme
and meter for sonnet modelling. We assess the quality of generated poems using
crowd and expert judgements. The stress and rhyme models perform very well, as
generated poems are largely indistinguishable from human-written poems. Expert
evaluation, however, reveals that a vanilla language model captures meter
implicitly, and that machine-generated poems still underperform in terms of
readability and emotion. Our research shows the importance expert evaluation
for poetry generation, and that future research should look beyond rhyme/meter
and focus on poetic language.

本文提出了一种联合架构，用于捕捉十四行诗的语言、韵律和节奏。我们使用众包和专家评估生成的诗歌的质量。研究表明专家评估对诗歌生成的重要性，并且未来的研究应该超越韵律、节奏，注重诗意语言。