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.

翻译：尽管基于深度学习的神经方法是当前自然语言处理（NLP）领域的最新技术水平，前神经算法与方法在近年来的NLP教科书和课程中仍占有一席之地。本文比较了澳大利亚和印度两门NLP入门课程，考察了Transformer与前神经方法在课程教学计划与考核中的平衡情况。我们还将其与CS1教育中“先面向对象”与“后面向对象”的争论进行了类比。我们观察到，前神经方法通过帮助学生建立对NLP问题、潜在解决方案乃至Transformer模型本身的直观理解，为学生学习增添了价值。尽管前神经方法并非最优技术，本文仍主张在当前的NLP课程中保留这些内容。