An Evaluation of State-of-the-Art Large Language Models for Sarcasm
  Detection
Juliann Zhou
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.