An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detection
This work addresses the challenge of accurately identifying sarcasm for sentiment analysis applications, but it is incremental as it applies existing models to a new dataset.
This study tackled the problem of sarcasm detection in natural language processing by evaluating state-of-the-art models like BERT and CASCADE on a Reddit corpus, finding that they outperformed baseline models with higher precision in interpreting contextualized language.
Sarcasm, as defined by Merriam-Webster, is the use of words by someone who means the opposite of what he is trying to say. In the field of sentimental analysis of Natural Language Processing, the ability to correctly identify sarcasm is necessary for understanding people's true opinions. Because the use of sarcasm is often context-based, previous research has used language representation models, such as Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), to identify sarcasm with contextual-based information. Recent innovations in NLP have provided more possibilities for detecting sarcasm. In BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin et al. (2018) introduced a new language representation model and demonstrated higher precision in interpreting contextualized language. As proposed by Hazarika et al. (2018), CASCADE is a context-driven model that produces good results for detecting sarcasm. This study analyzes a Reddit corpus using these two state-of-the-art models and evaluates their performance against baseline models to find the ideal approach to sarcasm detection.