Exploring T5 and RGAN for Enhanced Sarcasm Generation in NLP

Sarcasm, a figurative expression, plays a crucial role in human communication by conveying meanings often opposite to literal interpretation. Despite being common in daily conversations, sarcasm poses significant challenges for natural language processing (NLP) systems due to its intricate and context-sensitive nature. This study proposes a novel approach to sarcasm generation, leveraging advanced techniques in text augmentation, transfer learning, and evaluation. Our approach begins with pre-training a Transformer-based T5 model on a vast sarcastic and non-sarcastic text collection. Subsequently, we refine this model by fine-tuning it on an augmented dataset using Recurrent Generative Adversarial Networks (RGAN). Additionally, we incorporate top-p and top-k sampling techniques to promote diversity in generated text which is reflected in the robust BLEU and ROUGE scores of 0.82 and 0.8 indicating a high level of accuracy and quality in the generated sarcastic responses. These advancements highlight the potential for creating more nuanced and contextually appropriate sarcastic responses in chatbots, virtual assistants, and other AI-driven communication tools, ultimately enhancing human-computer interactions.