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Sarcasm Detection with Commonsense Knowledge
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AI
DOI：10.1109/TASLP.2021.3120601.png)
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Abstract
En 中文
Sarcasm is commonly used in today's social media platforms such as Twitter and Reddit. Sarcasm detection is necessary for analysing people's real sentiments as people usually use sarcasm to express a flipped emotion against the literal meaning. However, the current works neglect the fact that commonsense knowledge is crucial for sarcasm recognition. In this paper, we propose a novel architecture in deep learning for sarcasm detection by integrating commonsense knowledge. To be specific, we apply the pre-trained COMET model to generate relevant commonsense knowledge. Besides, we compare two kinds of knowledge selection strategies to investigate how commonsense knowledge influences performance. Finally, a knowledge-text integration module is designed to model both text and knowledge. The experimental results demonstrate our model's effectiveness on three datasets, including two Twitter datasets and a Reddit dataset.
Keywords:
Comets
Social networking (online)
Speech processing
Bit error rate
Medical services
Task analysis
Encoding
Commonsense knowledge
sarcasm detection
deep learning
knowledge selection
knowledge-text integration
Journal
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2.6K
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