Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation

TL;DR

This paper summarizes the results of a shared task on Chinese metaphor generation at NLPCC 2024, focusing on generating metaphors and identifying their components using machine learning, with participation from four teams.

Contribution

It introduces a new shared task on Chinese metaphor generation and component identification, providing a benchmark for future research in this area.

Findings

Four teams participated in the shared task.

Insights into effective methods for Chinese metaphor generation.

Baseline results and evaluation metrics are reported.

Abstract

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.