SQuAD上机器学习模型与BERT的比较研究（CS CL）---蔡秋纯

这项研究旨在提供对某些流行于机器学习的模型和斯坦福问题解答数据集（SQuAD）上的BERT模型的性能的比较分析。 分析表明，曾经是SQuAD上最先进的BERT模型，与其他模型相比，其准确性更高。 但是，即使仅使用100个样本，BERT也需要更长的执行时间。 这表明，随着准确性的提高，需要花费更多的时间来训练数据。 在初步的机器学习模型的情况下，完整数据的执行时间较短，但准确性受到影响。

原文标题：Comparative Study of Machine Learning Models and BERT on SQuAD

原文：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.

原文作者：Devshree Patel, Param Raval, Ratnam Parikh, Yesha Shastri

原文地址：https://arxiv.org/abs/2005.11313