# ConFiguRe: Exploring Discourse-level Chinese Figures of Speech

arXiv: https://arxiv.org/abs/2209.07678 · HTML: https://mlsanity.com/paper/2209.07678/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [Detecting Sarcasm in Multimodal Social Platforms](https://mlsanity.com/paper/1608.02289/) (2016)
- [A Report on the 2020 Sarcasm Detection Shared Task](https://mlsanity.com/paper/2005.05814/) (2020)
- [The Role of Conversation Context for Sarcasm Detection in Online Interactions](https://mlsanity.com/paper/1707.06226/) (2017)
- [Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context](https://mlsanity.com/paper/2006.06259/) (2020)

## Built on by (most cited first, 3 of 3)

- [Interpretable Chinese Metaphor Identification via LLM-Assisted MIPVU Rule Script Generation: A Comparative Protocol Study](https://mlsanity.com/paper/2603.10784/) (2026)
- [Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges](https://mlsanity.com/paper/2406.16674/) (2024)
- [Chinese Essay Rhetoric Recognition Using LoRA, In-context Learning and Model Ensemble](https://mlsanity.com/paper/2604.14167/) (2026)

## Abstract (arXiv)

Figures of speech, such as metaphor and irony, are ubiquitous in literature works and colloquial conversations. This poses great challenge for natural language understanding since figures of speech usually deviate from their ostensible meanings to express deeper semantic implications. Previous research lays emphasis on the literary aspect of figures and seldom provide a comprehensive exploration from a view of computational linguistics. In this paper, we first propose the concept of figurative unit, which is the carrier of a figure. Then we select 12 types of figures commonly used in Chinese, and build a Chinese corpus for Contextualized Figure Recognition (ConFiguRe). Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type. On ConFiguRe, three tasks, i.e., figure extraction, figure type classification and figure recognition, are designed and the state-of-the-art techniques are utilized to implement the benchmarks. We conduct thorough experiments and show that all three tasks are challenging for existing models, thus requiring further research. Our dataset and code are publicly available at https://github.com/pku-tangent/ConFiguRe.