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Recent advances in Natural Language Processing (NLP) have largely pushed deeptransformer-based models as the go-to state-of-the-art technique without muchregard to the production and utilization cost. Companies planning to adoptthese methods into their business face difficulties because of the lack ofmachine, data, and human resources to build them. We compare both theperformance and the cost of classical learning algorithms to the latest ones incommon sequence and text labeling tasks. In our industrial datasets, we findthat classical models often perform on par with deep neural ones despite thelower cost. We show the trade-off between performance gain and the cost acrossthe models to give more insights for AI-pivoting business. Further, we call formore research into low-cost models, especially for under-resourced languages.

Costs to Consider in Adopting NLP for Your Business