@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://preview.aclanthology.org/fix-sig-urls/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."
}