BERT-A: Fine-tuning BERT with Adapters and Data Augmentation

Sina Semnani, Kaushik Ram Sadagopan, Fatma Tlili

Abstract: We tackle the contextual question answering (QA) problem on the SQuAD 2.0 dataset. Our project has two main objectives. Firstly, we aim to build a model that achieves a reasonable performance while keeping the number of trainable parameters to a minimum. In this regard, we insert task-specific modules inside the pre-trained BERT model to control the flow of information between transformer blocks. Our proposed method for fine-tuning BERT achieves comparable performance to fine-tuning all BERT parameters while only training 0.57% of them. Secondly, we use our findings in the previous task to achieve an EM score of 78.36 and an F1 score of 81.44 on the test set (ranked 3rd on the PCE test leaderboard).