Papers by Yufeng Diao

4 papers
“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning (2024.lrec-main)

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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.
Outcome: The proposed model produces semantically coherent and humorous puns while ensuring both correctness and humor.
Giving Control Back to Models: Enabling Offensive Language Detection Models to Autonomously Identify and Mitigate Biases (2024.findings-emnlp)

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Challenge: Existing models often rely on specific words to predict offensive content, compromising model fairness and potentially exacerbates biases against vulnerable and minority groups.
Approach: They propose a bias self-awareness and data self-iteration framework to help models identify and mitigate biases by integrating multiple natural language processing techniques.
Outcome: The proposed framework reduces false positive rate of models in in-distribution and out-of-difference tests, enhances model accuracy and fairness, and shows promising performance improvements on larger datasets.
Hate Speech Detection Based on Sentiment Knowledge Sharing (2021.acl-long)

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Challenge: Existing methods for hate speech detection are stereotyped and biased . et al., a paper examining the effectiveness of multitask learning in hate speech recognition tasks .
Approach: They propose a hate speech detection framework based on sentiment knowledge sharing . they extract affective features of the target sentence and use sentiment features from external resources .
Outcome: The proposed model can detect hate speech over two public datasets.
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)

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Challenge: Homographic puns have a long history in human writing, widely used in written and spoken literature, which intended as jokes.
Approach: They propose a WordNet-encoded model to settle polysemy of homographic puns and a word weighted model for recognizing them.
Outcome: The proposed model can distinguish between homographic pun and non-homographic pun texts.

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