Smart Language: assigning service levels to copy editing – case study


Smart Language is TNQ’s AI-assisted product that assesses research manuscripts for language quality. This case study details how the global STM publisher Elsevier has used Smart Language in their workflow to improve quality, while potentially saving over $500,000 annually.

For STM publishers, exceptional language quality is non-negotiable. The crucial language editing process therefore needs to be robust and continually improving, while finding ways to boost efficiency gains. Smart Language assigns a language score and an editing level to manuscripts, so publishers can route them to the most appropriate editing teams/suppliers.

Built on a convolutional neural network, Smart Language leverages deep learning models and linguistically informed rule-based systems. It evaluates content based on sentence structure, parts-of-speech components, text sequences, spellings, and word similarity patterns on a sentence level, aggregating it all to the journal article or book chapter. Smart Language has been 3 years in the making, built from our 25 years of copy editing experience and in partnership with Enagoʼs Trinka, which was used as part of Smart Languageʼs core.

Business problem

Before Smart Language, Elsevier would assign a copy editing level to all articles in a journal, when in reality the language quality could vary greatly from article to article within every journal. Assessing every article in detail, however, takes significant manual effort.


TNQ’s product team worked with Elsevier to identify areas where we could largely automate language quality assessment, speed up turnaround time, and reduce costs. The ultimate goal was to improve the overall quality of these assessments, while preserving author satisfaction. Since November 2021, across 10,000 articles and 100,000 pages (ramping up to 20,000 articles in 2023), Smart Language has helped:

  • Move from a journal-level to an article-level workflow
  • Assign native English-speaking copy editors to articles needing “high” level editing
  • Improve turnaround time by breaking the process down by article
  • Reduce costs by eliminating redundant copy editing efforts

Key changes

Before Smart Language Since Smart Language
Manual assessment of manuscripts for language quality ML-based language profiler assesses for language quality
Copy editing service levels defined for entire journal Copy editing service levels defined by article
Fixed turnaround time and cost Faster turnaround time and reduced cost

Smart Language is part of TNQ’s Smart Central – a suite of microservices. 

About the author: Shanthi Krishnamoorthy is the Chief Domain Officer, heading R&D at TNQ. Her background in research and her love for books on science and human beings mean she is always encouraging all of us to read more! Shanthi has been with TNQ since the very beginning, lighting up the room when she walks in and leading with warmth and compassion. 

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