Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research
Ross Gruetzemacher, David Paradice

TL;DR
Transformer language models (TLMs) have the potential to significantly advance information systems research by improving text analysis, enabling new research topics, and supporting multilingual applications, thus broadening AI's impact on business and society.
Contribution
The paper reviews recent progress in TLMs and proposes how they can enhance and expand IS research, highlighting their advantages over traditional text mining methods.
Findings
TLMs outperform existing text mining techniques in various tasks.
Multilingual models enable higher quality analysis across languages.
Advanced models facilitate development of custom, powerful systems.
Abstract
AI is widely thought to be poised to transform business, yet current perceptions of the scope of this transformation may be myopic. Recent progress in natural language processing involving transformer language models (TLMs) offers a potential avenue for AI-driven business and societal transformation that is beyond the scope of what most currently foresee. We review this recent progress as well as recent literature utilizing text mining in top IS journals to develop an outline for how future IS research can benefit from these new techniques. Our review of existing IS literature reveals that suboptimal text mining techniques are prevalent and that the more advanced TLMs could be applied to enhance and increase IS research involving text data, and to enable new IS research topics, thus creating more value for the research community. This is possible because these techniques make it easier…
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Taxonomy
TopicsBig Data and Business Intelligence
