“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition (2020.acl-main)
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| Challenge: | Identifying and modeling puns is challenging as they involve implicit semantic or phonological tricks. |
| Approach: | They propose a method to detect puns in a sentence and then locate them in it . they propose to capture phonetic associations between the context and phonetic symbols . |
| Outcome: | The proposed method outperforms state-of-the-art methods in pun detection and location tasks. |
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Alessandro Zangari, Matteo Marcuzzo, Andrea Albarelli, Mohammad Taher Pilehvar, Jose Camacho-Collados
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| Challenge: | Existing methods for generating homographic puns are heavy-weighted due to the lack of training data. |
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Context-Situated Pun Generation (2022.emnlp-main)
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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng
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Comparison of Pun Detection Methods Using Japanese Pun Corpus (L18-1)
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| Challenge: | A sampling survey of typology and component ratio analysis in Japanese puns revealed that the type of Japanese pun that had the largest proportion was a pun type with two sound sequences. |
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“A good pun is its own reword”: Can Large Language Models Understand Puns? (2024.emnlp-main)
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| Challenge: | Existing studies on the understanding of puns in large language models (LLMs) have not explored the use of pun in creative writing and humor creation. |
| Approach: | They propose to use pun recognition, explanation and generation tasks to evaluate the capabilities of large language models (LLMs) they adopt automated evaluation metrics from prior research and introduce new evaluation methods and metrics that align more closely with human cognition. |
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Joint Detection and Location of English Puns (N19-1)
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| Challenge: | Existing research on puns has focused on understanding the meanings of words and phrases. |
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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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WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)
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Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang
| Challenge: | Homographic puns have a long history in human writing, widely used in written and spoken literature, which intended as jokes. |
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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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