CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task
Table 1: Experimental results for subtaskA on dev set.
['[BOLD] Models', '[BOLD] Accuracy']
In addition, we apply the ubiquitous architecture of transformers for classification, such as BERT, Albert, and RoBERTa. All these models allow us to pretrain a model on a large corpus of data, such as all Wikipedia articles and English book corpus, and then fine-tune them on downstream tasks. Looking at Table 1, we see RoBERTa outperforms all other models. We find out a significant difference when using fine-tuned Albert and BERT classification. Our idea to boost the performance of all these applied models is reframing the input of subtaskA as a binary classification task to the input of another downstream task, multiple choice questions. As a result, we show fine-tuned RoBERTa for multiple choice questions task gives better results than RoBERTa for classification problem on both dev and test set