# CERD: A Comprehensive Chinese Rhetoric Dataset for Rhetorical Understanding and Generation in Essays

arXiv: https://arxiv.org/abs/2409.19691 · HTML: https://mlsanity.com/paper/2409.19691/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model](https://mlsanity.com/paper/2003.01355/) (2020)
- [Neural Simile Recognition with Cyclic Multitask Learning and Local Attention](https://mlsanity.com/paper/1912.09084/) (2019)
- [Nominal Metaphor Generation with Multitask Learning](https://mlsanity.com/paper/2206.05195/) (2022)
- [Getting the Most out of Simile Recognition](https://mlsanity.com/paper/2211.05984/) (2022)
- [MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base](https://mlsanity.com/paper/2212.05254/) (2022)

## Built on by (most cited first, 2 of 2)

- [EssayBench: Evaluating Large Language Models in Multi-Genre Chinese Essay Writing](https://mlsanity.com/paper/2506.02596/) (2025)
- [Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble](https://mlsanity.com/paper/2604.14167/) (2026)

## Abstract (arXiv)

Existing rhetorical understanding and generation datasets or corpora primarily focus on single coarse-grained categories or fine-grained categories, neglecting the common interrelations between different rhetorical devices by treating them as independent sub-tasks. In this paper, we propose the Chinese Essay Rhetoric Dataset (CERD), consisting of 4 commonly used coarse-grained categories including metaphor, personification, hyperbole and parallelism and 23 fine-grained categories across both form and content levels. CERD is a manually annotated and comprehensive Chinese rhetoric dataset with five interrelated sub-tasks. Unlike previous work, our dataset aids in understanding various rhetorical devices, recognizing corresponding rhetorical components, and generating rhetorical sentences under given conditions, thereby improving the author's writing proficiency and language usage skills. Extensive experiments are conducted to demonstrate the interrelations between multiple tasks in CERD, as well as to establish a benchmark for future research on rhetoric. The experimental results indicate that Large Language Models achieve the best performance across most tasks, and jointly fine-tuning with multiple tasks further enhances performance.