@inproceedings{li-etal-2020-lijunyi,
title = "Lijunyi at {S}em{E}val-2020 Task 4: An {ALBERT} Model Based Maximum Ensemble with Different Training Sizes and Depths for Commonsense Validation and Explanation",
author = "Li, Junyi and
Wang, Bin and
Ding, Haiyan",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.69",
doi = "10.18653/v1/2020.semeval-1.69",
pages = "556--561",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Lijunyi at SemEval-2020 Task 4: An ALBERT Model Based Maximum Ensemble with Different Training Sizes and Depths for Commonsense Validation and Explanation
%A Li, Junyi
%A Wang, Bin
%A Ding, Haiyan
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F li-etal-2020-lijunyi
%X 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.
%R 10.18653/v1/2020.semeval-1.69
%U https://aclanthology.org/2020.semeval-1.69
%U https://doi.org/10.18653/v1/2020.semeval-1.69
%P 556-561
Markdown (Informal)
[Lijunyi at SemEval-2020 Task 4: An ALBERT Model Based Maximum Ensemble with Different Training Sizes and Depths for Commonsense Validation and Explanation](https://aclanthology.org/2020.semeval-1.69) (Li et al., SemEval 2020)
ACL