CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation

TL;DR

This paper presents a large-scale annotated Chinese metaphor corpus emphasizing grounds as CoT for improved metaphor generation, and evaluates generative models on this dataset.

Contribution

It introduces a novel Chinese metaphor dataset with ground annotations and a ground-focused approach for metaphor generation using Chain of Thoughts.

Findings

Models generate more creative metaphors with the dataset

Annotated corpus improves metaphor generation quality

Ground-based annotation enhances interpretability

Abstract

Metaphor is a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. This paper introduces a large-scale high quality annotated Chinese Metaphor Corpus, which comprises around 28K sentences drawn from a diverse range of Chinese literary sources, such as poems, prose, song lyrics, etc. To ensure the accuracy and consistency of our annotations, we introduce a comprehensive set of guidelines. These guidelines address the facets of metaphor annotation, including identifying tenors, vehicles, and grounds to handling the complexities of similes, personifications, juxtapositions, and hyperboles. Breaking tradition, our approach to metaphor generation emphasizes grounds and their distinct features rather than the conventional combination of tenors and vehicles. By integrating "ground" as a CoT (Chain of Thoughts) input, we are able to generate metaphors that resonate more with real-world intuition. We test generative models such as Belle, Baichuan, and Chinese-alpaca-33B using our annotated corpus. These models are able to generate creative and fluent metaphor sentences more frequently induced by selected samples from our dataset, demonstrating the value of our corpus for Chinese metaphor research. The code is available in https://github.com/JasonShao55/Chinese_Metaphor_Explanation.