UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts Using Transformers and Multi-Task Learning

George-Eduard Zaharia, George-Alexandru Vlad, Dumitru-Clementin Cercel, Traian Rebedea, Costin Chiru


Abstract
Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject. However, the task of managing such a process becomes noticeably more difficult when it is applied in cultures that tend to combine two languages in order to express ideas and thoughts. By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who are not familiar with this technique, but also for standard opinion mining algorithms. In this paper, we describe the systems developed by our team for SemEval-2020 Task 9 that aims to cover two well-known code-mixed languages: Hindi-English and Spanish-English. We intend to solve this issue by introducing a solution that takes advantage of several neural network approaches, as well as pre-trained word embeddings. Our approach (multlingual BERT) achieves promising performance on the Hindi-English task, with an average F1-score of 0.6850, registered on the competition leaderboard, ranking our team 16 out of 62 participants. For the Spanish-English task, we obtained an average F1-score of 0.7064 ranking our team 17th out of 29 participants by using another multilingual Transformer-based model, XLM-RoBERTa.
Anthology ID:
2020.semeval-1.179
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:
1322–1330
Language:
URL:
https://aclanthology.org/2020.semeval-1.179
DOI:
10.18653/v1/2020.semeval-1.179
Bibkey:
Cite (ACL):
George-Eduard Zaharia, George-Alexandru Vlad, Dumitru-Clementin Cercel, Traian Rebedea, and Costin Chiru. 2020. UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts Using Transformers and Multi-Task Learning. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1322–1330, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts Using Transformers and Multi-Task Learning (Zaharia et al., SemEval 2020)
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PDF:
https://aclanthology.org/2020.semeval-1.179.pdf