Abstract
This paper presents the best-performing approach alias “Adam Smith” for the SemEval-2023 Task 4: “Identification of Human Values behind Arguments”. The goal of the task was to create systems that automatically identify the values within textual arguments. We train transformer-based models until they reach their loss minimum or f1-score maximum. Ensembling the models by selecting one global decision threshold that maximizes the f1-score leads to the best-performing system in the competition. Ensembling based on stacking with logistic regressions shows the best performance on an additional dataset provided to evaluate the robustness (“Nahj al-Balagha”). Apart from outlining the submitted system, we demonstrate that the use of the large ensemble model is not necessary and that the system size can be significantly reduced.- Anthology ID:
- 2023.semeval-1.74
- 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:
- 532–541
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.74
- DOI:
- 10.18653/v1/2023.semeval-1.74
- Cite (ACL):
- Daniel Schroter, Daryna Dementieva, and Georg Groh. 2023. Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 532–541, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models (Schroter et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/fix-volume-bibkeys/2023.semeval-1.74.pdf