ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word Prediction

• URL: http://arxiv.org/abs/2104.01563v1
• Date: Sun, 4 Apr 2021 08:22:19 GMT
• Title: ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word Prediction
• Authors: Abhishek Mittal, Ashutosh Modi
• Abstract summary: We fine-tuned the pre-trained masked language models namely BERT and ALBERT. We tried multiple approaches and found that Masked Language Modeling(MLM) based approach works the best.
• Score: 2.482368922343792
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper describes our system for Task 4 of SemEval-2021: Reading Comprehension of Abstract Meaning (ReCAM). We participated in all subtasks where the main goal was to predict an abstract word missing from a statement. We fine-tuned the pre-trained masked language models namely BERT and ALBERT and used an Ensemble of these as our submitted system on Subtask 1 (ReCAM-Imperceptibility) and Subtask 2 (ReCAM-Nonspecificity). For Subtask 3 (ReCAM-Intersection), we submitted the ALBERT model as it gives the best results. We tried multiple approaches and found that Masked Language Modeling(MLM) based approach works the best.

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