Linguists are typically not unidimensional. In fact, linguists are some of the most multidisciplinary professionals, with innate code-switching abilities that transcend translation and interpreting.
As automation changes the way translators and interpreters work and make a living, job diversification becomes a distinct option for them as linguists, which is the foundation of what they do, applying different skills based on the job at hand.
Large language models (LLMs) have at the same time automated tasks traditionally done by linguists and created new opportunities to acquire new skills and master new trades. The key is to understand that massive amounts of all types of content are being created and localized, and these contents require human intervention at several points in the process.
Beyond editing AI-generated in-language content or post-editing AI-translated text, which are tasks currently trusted to translators, there are language-related jobs for which translation and interpreting skills and experience can serve as scaffoldings.
Below are some examples of job titles that have emerged as a result of the transition to AI-driven language services and 12 skills required of experts-in-the-loop beyond language fluency and conversion ability to qualify. These skills are based on a sample of 20 vacancy notices found on LocJobs, LinkedIn, and Indeed.
Examples of job titles: Language Content Analyst, AI Model Training Specialist, AI Language Specialist, Cultural Data Curator, Language Content Editor, Language Data Annotator, and Intelligence Copy Editor.
Skills required for these jobs include:
Organizational skills: Analysis of language data requires not just focus, but skills to distribute and prioritize tasks based on needs and co-dependencies
Research/Data collection skills
Knowledge of how raw data can be presented and classified (as in spreadsheets, tables, and advanced data functions)
Flexibility and adaptability to manage multiple systems efficiently
Advanced written and spoken language proficiency
Specialized knowledge of the type of content for which the data analysis / annotation / curation is conducted (e.g., audio, video, audiovisual, documentation, web content, etc.)
Specialized knowledge of the domain for which the data analysis / annotation / curation is conducted (e.g., pharmaceuticals, gaming, automotive, etc.)
Knowledge of translation evaluation metrics (e.g., BLEU, COMET, etc.)
Data annotation (tagging or labeling data to help machine learning algorithms understand and classify the information they process).
Data validation (making sure the data is correct and appropriate for the purpose at hand)
Prompt creation (knowing which words and phrases will render the best results out of AI chat-like models)
Terminology extraction, glossary creation, and term validation
Linguists can greatly benefit from targeted upskilling, including learning about data science, and tasks like annotation and model training. Learning these skills and understanding that human intelligence is still closing the [expert-in-the] loop in localization, will improve the chances for linguists to continue proving their value across all types of industries.
