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This paper presents a technical report of our submission to the 4th task ofSemEval-2021, titled: Reading Comprehension of Abstract Meaning. In this task,we want to predict the correct answer based on a question given a context.Usually, contexts are very lengthy and require a large receptive field from themodel. Thus, common contextualized language models like BERT miss finerepresentation and performance due to the limited capacity of the input tokens.To tackle this problem, we used the Longformer model to better process thesequences. Furthermore, we utilized the method proposed in the Longformerbenchmark on Wikihop dataset which improved the accuracy on our task data from23.01% and 22.95% achieved by the baselines for subtask 1 and 2, respectively,to 70.30% and 64.38%.

NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer