# Generating Classical Chinese Poems from Vernacular Chinese

arXiv: https://arxiv.org/abs/1909.00279 · HTML: https://mlsanity.com/paper/1909.00279/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 6 of 6)

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- [StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands](https://mlsanity.com/paper/2103.13439/) (2021)
- [Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition](https://mlsanity.com/paper/2205.14748/) (2022)
- [Isolating Culture Neurons in Multilingual Large Language Models](https://mlsanity.com/paper/2508.02241/) (2025)
- [Aesthetics of Sanskrit Poetry from the Perspective of Computational Linguistics: A Case Study Analysis on Siksastaka](https://mlsanity.com/paper/2308.07081/) (2023)
- [Sudowoodo: a Chinese Lyric Imitation System with Source Lyrics](https://mlsanity.com/paper/2308.04665/) (2023)

## Abstract (arXiv)

Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model. In this paper, we propose a novel task of generating classical Chinese poems from vernacular, which allows users to have more control over the semantic of generated poems. We adapt the approach of unsupervised machine translation (UMT) to our task. We use segmentation-based padding and reinforcement learning to address under-translation and over-translation respectively. According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models. Furthermore, we explored guidelines on how to write the input vernacular to generate better poems. Human evaluation showed our approach can generate high-quality poems which are comparable to amateur poems.