Language models and task-driven learning for sarcasm detectionDIMITRIEVIKJ, ALEKSANDAR (Avtor) Robnik Šikonja, Marko (Mentor) natural language processinglanguage modelssarcasm detectiontransformer architectureIn natural language processing, sarcasm detection determines whether a given text is sarcastic or not. It can have many real-world applications such as machine translation. In this work, we present three language modelling approaches and adapt them to the task of sarcasm detection. Two approaches are pretrained language models, BERT uses the encoder part of the transformer architecture and GPT-3 uses the decoder part of the transformer. The third method uses a newly-proposed task-driven learning technique TLM. We evaluated the methods using well-known metrics such as classification accuracy, precision and recall. GPT-3 performed the best in almost every aspect, with BERT being a close second. Our findings showed that TLM is very dependent on the task data and is therefore not suitable for a general task such as sarcasm detection.20222022-08-23 08:35:05Diplomsko delo/naloga138840sl