Abstract
We present a transfer learning system to perform a mixed Spanish-English sentiment classification task. Our proposal uses the state-of-the-art language model BERT and embed it within a ULMFiT transfer learning pipeline. This combination allows us to predict the polarity detection of code-mixed (English-Spanish) tweets. Thus, among 29 submitted systems, our approach (referred to as dplominop) is ranked 4th on the Sentimix Spanglish test set of SemEval 2020 Task 9. In fact, our system yields the weighted-F1 score value of 0.755 which can be easily reproduced — the source code and implementation details are made available.- Anthology ID:
- 2020.semeval-1.124
- 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:
- 963–967
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.124
- DOI:
- 10.18653/v1/2020.semeval-1.124
- Cite (ACL):
- Daniel Palomino and José Ochoa-Luna. 2020. Palomino-Ochoa at SemEval-2020 Task 9: Robust System Based on Transformer for Code-Mixed Sentiment Classification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 963–967, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- Palomino-Ochoa at SemEval-2020 Task 9: Robust System Based on Transformer for Code-Mixed Sentiment Classification (Palomino & Ochoa-Luna, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.semeval-1.124.pdf
- Data
- SentiMix