SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model

Yue Zhou, Bowei Wei, Jianyu Liu, Yang Yang


Abstract
Synonym and antonym practice are the most common practices in our early childhood. It correlated our known words to a better place deep in our intuition. At the beginning of life for a machine, we would like to treat the machine as a baby and built a similar training for it as well to present a qualified performance. In this paper, we present an ensemble model for sentence logistics classification, which outperforms the state-of-art methods. Our approach essentially builds on two models including ERNIE-M and DeBERTaV3. With cross validation and random seeds tuning, we select the top performance models for the last soft ensemble and make them vote for the final answer, achieving the top 6 performance.
Anthology ID:
2022.semeval-1.34
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
266–270
Language:
URL:
https://aclanthology.org/2022.semeval-1.34
DOI:
10.18653/v1/2022.semeval-1.34
Bibkey:
Cite (ACL):
Yue Zhou, Bowei Wei, Jianyu Liu, and Yang Yang. 2022. SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 266–270, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model (Zhou et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.34.pdf
Video:
 https://aclanthology.org/2022.semeval-1.34.mp4