Pairwise Learning Approach Using Siamese Neural Network For Contextual Sarcasm Detection
Nuzat Tasnim, Nasrin Sultana · 2023
The uprising trends of microblogging platforms has led to a shift in formulation of speech, with individuals opting for more attention-grabbing techniques, oftentimes resorting to sarcasm. Sarcasm, a form of irony used to convey contempt or humor, necessitates contextual understanding for detection. This work introduces a pairwise learning approach for context-based sarcasm detection on microblogging platforms. The proposed approach utilizes a variant of the siamese neural network trained on conversation-response pairs. Pre-trained BERT-Large is employed to extract features from input pairs, followed by separate branches of LSTM network. Outputs of them were merged before feeding it to the multi-layer LSTM and hidden layers. Unlike traditional siamese models, the intermediate outputs are merged and processed further to serve the fine-tuning purpose and eventually it aims to classify input data as sarcastic or non-sarcastic. Through extensive experimental evaluation conducted on Twitter and Reddit datasets, we achieved notable F1-scores of 0.73 and 0.66, respectively. Although our results demonstrate the system's efficacy, they did not surpass existing state-of-the-art systems. While the proposed approach provides a promising solution for sarcasm detection in microblogging platforms, further investigations are needed to address the existing limitations and optimize the system’s performance. This research contributes to the understanding of attention-grabbing techniques in Natural Language Processing (NLP) and lays the groundwork for future advancements in sarcasm detection.