Abstract
This paper describes our system submitted for SemEval Task 3: Presupposed Taxonomies: Evaluating Neural Network Semantics (Zamparelli et al., 2022). We participated in both the binary classification and the regression subtask. Target sentences are classified according to their taxonomical relation in subtask 1 and according to their acceptability judgment in subtask 2. Our approach in both subtasks is based on a neural network BERT model. We used separate models for the three languages covered by the task, English, French, and Italian. For the second subtask, we used median averaging to construct an ensemble model. We ranked 15th out of 21 groups for subtask 1 (F1-score: 77.38%) and 11th out of 17 groups for subtask 2 (RHO: 0.078).- Anthology ID:
- 2022.semeval-1.36
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 282–290
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.36
- DOI:
- 10.18653/v1/2022.semeval-1.36
- Cite (ACL):
- Karl Vetter, Miriam Segiet, and Klara Lennermann. 2022. KaMiKla at SemEval-2022 Task 3: AlBERTo, BERT, and CamemBERT—Be(r)tween Taxonomy Detection and Prediction. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 282–290, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- KaMiKla at SemEval-2022 Task 3: AlBERTo, BERT, and CamemBERT—Be(r)tween Taxonomy Detection and Prediction (Vetter et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.36.pdf