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

TL;DR

This paper evaluates the effectiveness of advanced NLP models, specifically BERT and CASCADE, in detecting sarcasm within Reddit data, comparing their performance to traditional methods to identify the most effective approach.

Contribution

It provides a comparative analysis of BERT and CASCADE models for sarcasm detection, highlighting their strengths over baseline models in a real-world Reddit dataset.

Findings

BERT outperforms baseline models in sarcasm detection accuracy.

CASCADE shows competitive performance with context-driven features.

Both models improve sarcasm detection over traditional methods.

Abstract

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.