UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection
Emmanuel Osei-Brefo, Huizhi Liang · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
Sarcasm has gained notoriety for being difficult to detect by machine learning systems due to its figurative nature.In this paper, Bidirectional Encoder Representations from Transformers (BERT) model has been used with ensemble loss made of cross-entropy loss and negative log-likelihood loss to classify whether a given sentence is in English and Arabic tweets are sarcastic or not.From the results obtained in the experiments, our proposed BERT with ensemble loss achieved superior performance when applied to English and Arabic test datasets.For the validation dataset, our model performed better on the Arabic dataset but failed to outperform the baseline method (made of BERT with only a single loss function) when applied on the English validation set.