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Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis - 2021

Research Area:  Machine Learning


Sarcasm detection and sentiment analysis are important tasks in Natural Language Understanding. Sarcasm is a type of expression where the sentiment polarity is flipped by an interfering factor. In this study, we exploited this relationship to enhance both tasks by proposing a multi-task learning approach using a combination of static and contextualised embeddings. Our proposed system achieved the best result in the sarcasm detection subtask.

Author(s) Name:  Abdullah I. Alharbi, Mark Lee

Journal name:  

Conferrence name:  Proceedings of the Sixth Arabic Natural Language Processing Workshop

Publisher name:  Association for Computational Linguistics


Volume Information: