A knowledge-augmented neural network model for sarcasm detection

Ren, Yafeng; Wang, Zilin; Peng, Qiong; Ji, Donghong

doi:10.1016/j.ipm.2023.103521

Cited by 6 publications

(1 citation statement)

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“…To avoid the noise introduced by manually constructing static graphs, a method of iterative incongruity graph learning has been proposed to alleviate this problem [44]. Additionally, some studies have utilized external knowledge and information to enhance the model's semantic understanding of sarcastic text [45,46].…”

Section: Sarcasm Detectionmentioning

confidence: 99%

Enhancing Cross-Lingual Sarcasm Detection by a Prompt Learning Framework with Data Augmentation and Contrastive Learning

An,

Yan,

Zuo

et al. 2024

Electronics

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Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm detection tasks, a zero-shot cross-lingual transfer learning method is proposed in this paper. The proposed approach is based on prompt learning and aims to assist the model with understanding downstream tasks through prompts. Specifically, the model uses prompt templates to construct training data into cloze-style questions and then trains them using a pre-trained cross-lingual language model. Combining data augmentation and contrastive learning can further improve the capacity of the model for cross-lingual transfer learning. To evaluate the performance of the proposed model, we utilize a publicly accessible sarcasm dataset in English as training data in a zero-shot cross-lingual setting. When tested with Chinese as the target language for transfer, our model achieves F1-scores of 72.14% and 76.7% on two test datasets, outperforming the strong baselines by significant margins.

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Section: Sarcasm Detectionmentioning

confidence: 99%