Abstract
In this paper, we present an approach for sentiment analysis in code-mixed language on twitter defined in SemEval-2020 Task 9. Our team (referred as LiangZhao) employ different multilingual models with weighted loss focused on complexity of code-mixing in sentence, in which the best model achieved f1-score of 0.806 and ranked 1st of subtask- Sentimix Spanglish. The performance of method is analyzed and each component of our architecture is demonstrated.- Anthology ID:
- 2020.semeval-1.126
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 975–980
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.126
- DOI:
- 10.18653/v1/2020.semeval-1.126
- Cite (ACL):
- Yili Ma, Liang Zhao, and Jie Hao. 2020. XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 975–980, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language (Ma et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.semeval-1.126.pdf