# Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation

arXiv: https://arxiv.org/abs/2408.04378 · HTML: https://mlsanity.com/paper/2408.04378/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Yi: Open Foundation Models by 01.AI](https://mlsanity.com/paper/2403.04652/) (2024)
- [Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model](https://mlsanity.com/paper/2404.04167/) (2024)
- [CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation](https://mlsanity.com/paper/2301.00395/) (2023)
- [FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning](https://mlsanity.com/paper/2302.04834/) (2023)
- [Metaphor Detection with Effective Context Denoising](https://mlsanity.com/paper/2302.05611/) (2023)
- [Metaphor Detection via Explicit Basic Meanings Modelling](https://mlsanity.com/paper/2305.17268/) (2023)
- [CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation](https://mlsanity.com/paper/2402.13145/) (2024)
- [MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language](https://mlsanity.com/paper/2406.13698/) (2024)
- [The Secret of Metaphor on Expressing Stronger Emotion](https://mlsanity.com/paper/2301.13042/) (2023)
- [Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection](https://mlsanity.com/paper/1709.00575/) (2017)
- [Finding Challenging Metaphors that Confuse Pretrained Language Models](https://mlsanity.com/paper/2401.16012/) (2024)

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

- [LaiDA: Linguistics-aware In-context Learning with Data Augmentation for Metaphor Components Identification](https://mlsanity.com/paper/2408.05404/) (2024)
- [Chinese Metaphor Recognition Using a Multi-stage Prompting Large Language Model](https://mlsanity.com/paper/2408.09177/) (2024)

## Abstract (arXiv)

This paper presents the results of the shared task on Chinese metaphor generation, hosted at the 13th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC 2024). The goal of this shared task is to generate Chinese metaphors using machine learning techniques and effectively identifying basic components of metaphorical sentences. It is divided into two subtasks: 1) Metaphor Generation, which involves creating a metaphor from a provided tuple consisting of TENOR, GROUND, and VEHICLE. The goal here is to synthesize a metaphor that connects the subject (i.e. TENOR) with the object (i.e. VEHICLE), guided by the concept of the GROUND. 2) Metaphor Components Identification, which extracts the most fitting TENORs, GROUNDs, and VEHICLEs from a metaphorical sentence. This component requires the identification of the most fitting metaphor elements that correspond to the specified grounds. In addition to overall results, we report on the setup and insights from the metaphor generation shared task, which attracted a total of 4 participating teams across both subtasks.