RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations

Frank van den Berg, Gijs Danoe, Esther Ploeger, Wessel Poelman, Lukas Edman, Tommaso Caselli


Abstract
This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics. This task is focused on correctly recognizing taxonomic word relations in English, French and Italian. We developed various datageneration techniques that expand the originally provided train set and show that all methods increase the performance of modelstrained on these expanded datasets. Our final system outperformed the baseline system from the task organizers by achieving an average macro F1 score of 79.6 on all languages, compared to the baseline’s 67.4.
Anthology ID:
2022.semeval-1.31
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
247–254
Language:
URL:
https://aclanthology.org/2022.semeval-1.31
DOI:
10.18653/v1/2022.semeval-1.31
Bibkey:
Cite (ACL):
Frank van den Berg, Gijs Danoe, Esther Ploeger, Wessel Poelman, Lukas Edman, and Tommaso Caselli. 2022. RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 247–254, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations (van den Berg et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.31.pdf
Video:
 https://aclanthology.org/2022.semeval-1.31.mp4