A Mixture of Experts Approach for Refined Sarcasm Detection in Text

The topic of sarcasm detection has always been hot in natural language processing, which leverages the subtle contextual and linguistic cues of figurative language to identify and interpret it. Accurately detecting sarcasm is crucial for revealing the true sentiment expressed behind statements and thus to improve the effectiveness of sentiment analysis algorithms. This work introduces a new method of improving the accuracy of identifying sarcasm based on a Mixture of Experts scheme. The model is structured such that each expert network specializes in different aspects of sarcasm, captures distinct implicit features, and collectively detects sarcasm. Such structure enables a comprehensive understanding of context and sarcasm-related nuances. The model was evaluated on dialogue sarcasm datasets and achieved optimal performance. This improvement in detection performance is attributed to the model's ability to utilize expert specialization to better distinguish sarcastic content in various linguistic structures and contexts. It also shows promising results in terms of scalability to other datasets with less computational efforts and faster inference compared to larger and more complex language models, making this method a useful tool in other contexts social media monitoring and sentiment analysis. This study attempts to contribute to the further research in sarcasm detection and offer a foundation for future work on sentiment analysis.