Lijunyi at SemEval-2020 Task 4: An ALBERT Model Based Maximum Ensemble with Different Training Sizes and Depths for Commonsense Validation and Explanation

Junyi Li, Bin Wang, Haiyan Ding


Abstract
This article describes the system submitted to SemEval 2020 Task 4: Commonsense Validation and Explanation. We only participated in the subtask A, which is mainly to distinguish whether the sentence has meaning. To solve this task, we mainly used ALBERT model-based maximum ensemble with different training sizes and depths. To prove the validity of the model to the task, we also used some other neural network models for comparison. Our model achieved the accuracy score of 0.938(ranked 10/41) in subtask A.
Anthology ID:
2020.semeval-1.69
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
*SEMEVAL | COLING
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
556–561
Language:
URL:
https://www.aclweb.org/anthology/2020.semeval-1.69
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.semeval-1.69.pdf