Papers › Comparative Study of Machine Learning Models and BERT on SQuAD

Comparative Study of Machine Learning Models and BERT on SQuAD

22 May 2020arXiv:2005.11313archive 2025-07-28

Devshree Patel, Param Raval, Ratnam Parikh, Yesha Shastri

This study aims to provide a comparative analysis of performance of certain models popular in machine learning and the BERT model on the Stanford Question Answering Dataset (SQuAD). The analysis shows that the BERT model, which was once state-of-the-art on SQuAD, gives higher accuracy in comparison to other models. However, BERT requires a greater execution time even when only 100 samples are used. This shows that with increasing accuracy more amount of time is invested in training the data. Whereas in case of preliminary machine learning models, execution time for full data is lower but accuracy is compromised.

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devshree07/BERT-on-SQuAD mentioned on GitHub report

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BIG-bench Machine Learning Question Answering

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Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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