While neural approaches using deep learning are the state-of-the-art for natural language processing (NLP) today, pre-neural algorithms and approaches still find a place in NLP textbooks and courses of recent years. In this paper, we compare two introductory NLP courses taught in Australia and India, and examine how Transformer and pre-neural approaches are balanced within the lecture plan and assessments of the courses. We also draw parallels with the objects-first and objects-later debate in CS1 education. We observe that pre-neural approaches add value to student learning by building an intuitive understanding of NLP problems, potential solutions and even Transformer-based models themselves. Despite pre-neural approaches not being state-of-the-art, the paper makes a case for their inclusion in NLP courses today.

比较介绍了澳大利亚和印度两门自然语言处理（NLP）课程，并研究了Transformer和早期神经算法在课程的讲座计划和评估中的平衡。观察到早期神经算法对学生学习有益，帮助他们建立对NLP问题、潜在解决方案甚至基于Transformer的模型的直观理解。尽管早期神经算法不是最先进的技术，但本文支持将其纳入当今的NLP课程。

自然语言处理教育中预神经网络方法的相关性