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Summary of Exploring Chinese Humor Generation: a Study on Two-part Allegorical Sayings, by Rongwu Xu


Exploring Chinese Humor Generation: A Study on Two-Part Allegorical Sayings

by Rongwu Xu

First submitted to arxiv on: 16 Mar 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper investigates the ability of state-of-the-art language models to comprehend and generate Chinese humor, particularly focusing on training them to create allegorical sayings. The researchers employ two prominent training methods: fine-tuning a medium-sized language model and prompting a large one. A novel fine-tuning approach is proposed, incorporating fused Pinyin embeddings to consider homophones and contrastive learning with synthetic hard negatives to distinguish humor elements. Human-annotated results show that these models can generate humorous allegorical sayings, with prompting proving to be a practical and effective method for generating Chinese humor. The study highlights the challenges of modeling humor in Chinese language and identifies room for improvement in generating allegorical sayings that match human creativity.
Low GrooveSquid.com (original content) Low Difficulty Summary
This research paper looks at how well computer models can understand and create funny jokes in Chinese. It’s a tricky task because humor is different across cultures, and Chinese has its own unique way of using language to be humorous. The researchers tried two ways to train the models: fine-tuning a medium-sized model and prompting a large one. They found that both methods worked well for generating humorous sayings, but there’s still room for improvement in making them sound as creative and funny as humans do.

Keywords

» Artificial intelligence  » Fine tuning  » Language model  » Prompting