| Challenge: | Existing methods for generating pun sentences with word senses lack large-scale corpus for supervised learning . a pun is a clever and amusing use of a word with two meanings (word senses) |
| Approach: | They propose an adversarial generative network for pun generation with a generator and a discriminator to distinguish between generated pun sentences and real sentences with specific word senses. |
| Outcome: | The proposed network generates sentences that are more ambiguous and diverse in both automatic and human evaluation. |
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| Challenge: | Existing methods for generating humorous puns are limited and require a broad spectrum of commonsense and worldly skills. |
| Approach: | They propose a GAN-based approach that employs semantic pruning and contrastive learning to generate humorous puns using a model that captures the semantic nuances of puns. |
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| Challenge: | Existing studies on extractive keyphrases have shown promising results, but the results suggest that there is room for improvement. |
| Approach: | They propose a new keyphrase generation approach using Generative Adversarial Networks (GANs) their model produces a sequence of keyphrases and a discriminator distinguishes between human-curated and machine-generated keyphrase. |
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A Neural Approach to Pun Generation (P18-1)
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| Challenge: | generating puns with artificial intelligence techniques requires manual training and templates. |
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An End-to-End Generative Architecture for Paraphrase Generation (D19-1)
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A Survey of Pun Generation: Datasets, Evaluations and Methodologies (2025.findings-emnlp)
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| Challenge: | Pun generation aims to modify linguistic elements in text to produce humour or evoke double meanings. |
| Approach: | They propose to review pun generation datasets and methods across different stages . pun generation aims to produce humour or evoke double meanings . |
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Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation (N19-1)
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| Challenge: | Text generation with generative adversarial networks (GANs) can be divided into text-based and code-based categories depending on the type of signals used for discrimination. |
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Pun Generation with Surprise (N19-1)
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| Challenge: | In this paper, we explore creative generation with a focus on puns. |
| Approach: | They propose an unsupervised approach to generating puns using lots of raw text and a surprisal principle. |
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AmbiPun: Generating Humorous Puns with Ambiguous Context (2022.naacl-main)
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| Challenge: | Existing methods for generating homographic puns are heavy-weighted due to the lack of training data. |
| Approach: | They propose a way to generate pun sentences that does not require training on existing puns. |
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A Unified Framework for Pun Generation with Humor Principles (2022.findings-emnlp)
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| Challenge: | Existing models for generating homophonic and homographic puns lack the linguistic attributes of successful puns to resolve the split-up in existing work. |
| Approach: | They propose a framework to generate both homophonic and homographic puns to resolve the split-up in existing works by incorporating three linguistic attributes of puns into the language models: ambiguity, distinctiveness, and surprise. |
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