Abstract
This paper describes our contribution to SemEval-23 Shared Task 12: ArfiSenti. The task consists of several sentiment classification subtasks for rarely studied African languages to predict positive, negative, or neutral classes of a given Twitter dataset. In our system we utilized three different models; FastText, MultiLang Transformers, and Language-Specific Transformers to find the best working model for the classification challenge. We experimented with mentioned models and mostly reached the best prediction scores using the Language Specific Transformers. Our best-submitted result was ranked 3rd among submissions for the Amharic language, obtaining an F1 score of 0.702 behind the second-ranked system.- Anthology ID:
- 2023.semeval-1.71
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 512–516
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.71
- DOI:
- 10.18653/v1/2023.semeval-1.71
- Cite (ACL):
- Selman Delil and Birol Kuyumcu. 2023. Sefamerve at SemEval-2023 Task 12: Semantic Evaluation of Rarely Studied Languages. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 512–516, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Sefamerve at SemEval-2023 Task 12: Semantic Evaluation of Rarely Studied Languages (Delil & Kuyumcu, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.semeval-1.71.pdf