CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914687558352896 |
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| author | Shao, Yujie Yao, Xinrong Qu, Xingwei Lin, Chenghua Wang, Shi Huang, Stephen W. Zhang, Ge Fu, Jie |
| author_facet | Shao, Yujie Yao, Xinrong Qu, Xingwei Lin, Chenghua Wang, Shi Huang, Stephen W. Zhang, Ge Fu, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13145 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation Shao, Yujie Yao, Xinrong Qu, Xingwei Lin, Chenghua Wang, Shi Huang, Stephen W. Zhang, Ge Fu, Jie Computation and Language Artificial Intelligence 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. |
| title | CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.13145 |