FiSSA at SemEval-2020 Task 9: Fine-tuned for Feelings
Bertelt Braaksma, Richard Scholtens, Stan van Suijlekom, Remy Wang, Ahmet Üstün
Abstract
In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data in the SemEval-2020 Task 9. We investigate performance of various pre-trained Transformer models by using different fine-tuning strategies. We explore both monolingual and multilingual models with the standard fine-tuning method. Additionally, we propose a custom model that we fine-tune in two steps: once with a language modeling objective, and once with a task-specific objective. Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score with 0.537 on development data and 0.739 on test data. With this score, our team jupitter placed tenth overall in the competition.- Anthology ID:
- 2020.semeval-1.165
- 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:
- 1239–1246
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.165
- DOI:
- 10.18653/v1/2020.semeval-1.165
- Cite (ACL):
- Bertelt Braaksma, Richard Scholtens, Stan van Suijlekom, Remy Wang, and Ahmet Üstün. 2020. FiSSA at SemEval-2020 Task 9: Fine-tuned for Feelings. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1239–1246, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- FiSSA at SemEval-2020 Task 9: Fine-tuned for Feelings (Braaksma et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2020.semeval-1.165.pdf
- Code
- barfsma/FiSSA