Abstract
In this paper, we describe our system submitted for SemEval 2020 Task 9, Sentiment Analysis for Code-Mixed Social Media Text alongside other experiments. Our best performing system is a Transfer Learning-based model that fine-tunes XLM-RoBERTa, a transformer-based multilingual masked language model, on monolingual English and Spanish data and Spanish-English code-mixed data. Our system outperforms the official task baseline by achieving a 70.1% average F1-Score on the official leaderboard using the test set. For later submissions, our system manages to achieve a 75.9% average F1-Score on the test set using CodaLab username “ahmed0sultan”.- Anthology ID:
- 2020.semeval-1.181
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1342–1347
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.181
- DOI:
- 10.18653/v1/2020.semeval-1.181
- Cite (ACL):
- Ahmed Sultan, Mahmoud Salim, Amina Gaber, and Islam El Hosary. 2020. WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1342–1347, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers (Sultan et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.semeval-1.181.pdf