Teaching NLP outside Linguistics and Computer Science classrooms: Some challenges and some opportunities

TL;DR

This paper discusses the challenges and opportunities of teaching NLP to diverse audiences outside traditional computer science and linguistics classrooms, highlighting issues faced by instructors and researchers.

Contribution

It provides insights from classroom experiences on teaching NLP to interdisciplinary students and identifies key challenges for educators and tool developers.

Findings

Diverse backgrounds of students impact NLP teaching strategies.

Lack of related courses creates challenges for learners and instructors.

Identifies areas for research and tool development to support interdisciplinary NLP education.

Abstract

NLP's sphere of influence went much beyond computer science research and the development of software applications in the past decade. We see people using NLP methods in a range of academic disciplines from Asian Studies to Clinical Oncology. We also notice the presence of NLP as a module in most of the data science curricula within and outside of regular university setups. These courses are taken by students from very diverse backgrounds. This paper takes a closer look at some issues related to teaching NLP to these diverse audiences based on my classroom experiences, and identifies some challenges the instructors face, particularly when there is no ecosystem of related courses for the students. In this process, it also identifies a few challenge areas for both NLP researchers and tool developers.