FIT BUT at SemEval-2023 Task 12: Sentiment Without Borders - Multilingual Domain Adaptation for Low-Resource Sentiment Classification

Maksim Aparovich, Santosh Kesiraju, Aneta Dufkova, Pavel Smrz

Abstract
This paper presents our proposed method for SemEval-2023 Task 12, which focuses on sentiment analysis for low-resource African languages. Our method utilizes a language-centric domain adaptation approach which is based on adversarial training, where a small version of Afro-XLM-Roberta serves as a generator model and a feed-forward network as a discriminator. We participated in all three subtasks: monolingual (12 tracks), multilingual (1 track), and zero-shot (2 tracks). Our results show an improvement in weighted F1 for 13 out of 15 tracks with a maximum increase of 4.3 points for Moroccan Arabic compared to the baseline. We observed that using language family-based labels along with sequence-level input representations for the discriminator model improves the quality of the cross-lingual sentiment analysis for the languages unseen during the training. Additionally, our experimental results suggest that training the system on languages that are close in a language families tree enhances the quality of sentiment analysis for low-resource languages. Lastly, the computational complexity of the prediction step was kept at the same level which makes the approach to be interesting from a practical perspective. The code of the approach can be found in our repository.
Anthology ID:
2023.semeval-1.209
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:
1518–1524
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/2023.semeval-1.209/
DOI:
10.18653/v1/2023.semeval-1.209
Bibkey:
Cite (ACL):
Maksim Aparovich, Santosh Kesiraju, Aneta Dufkova, and Pavel Smrz. 2023. FIT BUT at SemEval-2023 Task 12: Sentiment Without Borders - Multilingual Domain Adaptation for Low-Resource Sentiment Classification. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1518–1524, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
FIT BUT at SemEval-2023 Task 12: Sentiment Without Borders - Multilingual Domain Adaptation for Low-Resource Sentiment Classification (Aparovich et al., SemEval 2023)
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PDF:
https://preview.aclanthology.org/icon-24-ingestion/2023.semeval-1.209.pdf
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