系统梳理谐音梗生成的数据集、方法与评估体系
A Survey of Pun Generation: Datasets, Evaluations and Methodologies
- 按传统方法、深度学习、预训练模型分阶段综述生成技术
- 归纳自动化与人工评估指标，涵盖语义双关与上下文一致性
- 适合自然语言生成、幽默计算研究者参考
谐音梗生成旨在创造性地改写文本中的语言元素，以产生幽默或引发多重含义，同时保持语义连贯与语境恰当，广泛应用于创意写作与各类媒体娱乐场景。尽管该领域在计算语言学中已受关注，但尚无专门综述系统梳理其发展。本文全面回顾了谐音梗生成领域的数据集与方法，涵盖传统方法、深度学习及预训练语言模型等不同阶段的技术演进。同时总结了自动评估与人工评估指标，用于衡量生成结果的质量。最后，分析当前研究挑战并提出未来发展方向。
原文摘要 · Abstract (English)
Pun generation seeks to creatively modify linguistic elements in text to produce humour or evoke double meanings. It also aims to preserve coherence and contextual appropriateness, making it useful in creative writing and entertainment across various media and contexts. Although pun generation has received considerable attention in computational linguistics, there is currently no dedicated survey that systematically reviews this specific area. To bridge this gap, this paper provides a comprehensive review of pun generation datasets and methods across different stages, including conventional approaches, deep learning techniques, and pre-trained language models. Additionally, we summarise both automated and human evaluation metrics used to assess the quality of pun generation. Finally, we discuss the research challenges and propose promising directions for future work.
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