@inproceedings{al-bashabsheh-etal-2020-nlp,
title = "{NLP}@{JUST} at {S}em{E}val-2020 Task 4: Ensemble Technique for {BERT} and Roberta to Evaluate Commonsense Validation",
author = "Al-Bashabsheh, Emran and
Abu Aqouleh, Ayah and
AL-Smadi, Mohammad",
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-1.72",
doi = "10.18653/v1/2020.semeval-1.72",
pages = "574--579",
abstract = "This paper presents the work of the NLP@JUST team at SemEval-2020 Task 4 competition that related to commonsense validation and explanation (ComVE) task. The team participates in sub-taskA (Validation) which related to validation that checks if the text is against common sense or not. Several models have trained (\textit{i.e. Bert, XLNet, and Roberta}), however, the main models used are the RoBERTa-large and BERT Whole word masking. As well as, we utilized the results from both models to generate final prediction by using the average Ensemble technique, that used to improve the overall performance. The evaluation result shows that the implemented model achieved an accuracy of 93.9{\%} obtained and published at the post-evaluation result on the leaderboard.",
}
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<abstract>This paper presents the work of the NLP@JUST team at SemEval-2020 Task 4 competition that related to commonsense validation and explanation (ComVE) task. The team participates in sub-taskA (Validation) which related to validation that checks if the text is against common sense or not. Several models have trained (i.e. Bert, XLNet, and Roberta), however, the main models used are the RoBERTa-large and BERT Whole word masking. As well as, we utilized the results from both models to generate final prediction by using the average Ensemble technique, that used to improve the overall performance. The evaluation result shows that the implemented model achieved an accuracy of 93.9% obtained and published at the post-evaluation result on the leaderboard.</abstract>
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%0 Conference Proceedings
%T NLP@JUST at SemEval-2020 Task 4: Ensemble Technique for BERT and Roberta to Evaluate Commonsense Validation
%A Al-Bashabsheh, Emran
%A Abu Aqouleh, Ayah
%A AL-Smadi, Mohammad
%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 al-bashabsheh-etal-2020-nlp
%X This paper presents the work of the NLP@JUST team at SemEval-2020 Task 4 competition that related to commonsense validation and explanation (ComVE) task. The team participates in sub-taskA (Validation) which related to validation that checks if the text is against common sense or not. Several models have trained (i.e. Bert, XLNet, and Roberta), however, the main models used are the RoBERTa-large and BERT Whole word masking. As well as, we utilized the results from both models to generate final prediction by using the average Ensemble technique, that used to improve the overall performance. The evaluation result shows that the implemented model achieved an accuracy of 93.9% obtained and published at the post-evaluation result on the leaderboard.
%R 10.18653/v1/2020.semeval-1.72
%U https://aclanthology.org/2020.semeval-1.72
%U https://doi.org/10.18653/v1/2020.semeval-1.72
%P 574-579
Markdown (Informal)
[NLP@JUST at SemEval-2020 Task 4: Ensemble Technique for BERT and Roberta to Evaluate Commonsense Validation](https://aclanthology.org/2020.semeval-1.72) (Al-Bashabsheh et al., SemEval 2020)
ACL