Custom Machine Translation Solutions
Translating data for machine learning requires more than just native speakers. Our linguists are trained in data collection translation projects to adapt their translation style to the task and to follow the ML rules as specified. You get a consistent quality data set.
We make your data more usable with data translation for Natural Language Processing (NLP) training models.
Clean data sets are vital when training AI to respond to written and verbal language. Using supervised data collection, Argos delivers multilingual data sets of text, voice, and image data suitable for test and training sets. Argos has an extensive pool of qualified resources. We can scale to 1000+ linguists for quality data set translation in 2 to 4 weeks.
Quality AI needs good, clean data sets. We collect data based on your system's unique requirements. With qualified resources around the globe, we rapidly scale according to your project and cover more than 75 languages. Our human-in-the-loop process provides better data than scraping or crowdsourcing alone. All of this means better performance for your machine learning systems.
Machine Translation (MT) offers fast turnaround times, large volumes, and low costs. Argos creates custom MT solutions to meet our clients' needs.
Argos provides a customized MT engine that gets smarter as it learns customer preferences. We work with you to determine solutions for your content from baseline engine selection to workflow setup. We have a detailed quality assurance process for optimizing time and cost savings at the right level of quality.
Machine Translation Quality Evaluation
Trained linguists review your machine-translated context to ensure it meets quality standards.
Machine translation can work for certain types of content. Yet, in many cases, quality lags behind human standards. Our quality assessment team helps you take advantage of lower costs without sacrificing quality. Argos has qualified linguists in over 75 languages to evaluate your machine translation.
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