Abstract
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.- Anthology ID:
- 2021.wanlp-1.39
- Volume:
- Proceedings of the Sixth Arabic Natural Language Processing Workshop
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv, Ukraine (Virtual)
- Editors:
- Nizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 318–322
- Language:
- URL:
- https://aclanthology.org/2021.wanlp-1.39
- DOI:
- Cite (ACL):
- Abdullah I. Alharbi and Mark Lee. 2021. Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 318–322, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
- Cite (Informal):
- Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis (Alharbi & Lee, WANLP 2021)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2021.wanlp-1.39.pdf