Expand Translation Scope
DefinedCrowd’s proprietary translation workflows combined with human translators can source, translate and validate machine translation datasets of bilingual pairs.
Post Editing Machine Translation
Use human translators to edit or revise machine translated text.
Machine Translation Evaluation
Evaluate the quality of machine translation outputs to help improve and tune existing models.
Custom MT Datasets
Sourced and validated bi-lingual pairs using our translation workflows.
Quality Guarantee
Translating one language into another can be a subjective task, making it difficult for a machine to accomplish accurately. For translation projects, professional linguists and translators are selected based on nativeness in a target language and fluency in a source language. They are also measured by:
AccuracyMaintaining the intent or meaning of the source phrase. |
Spelling and GrammarVerifying proper spelling, grammar, and syntax. |
Cultural RelevanceVerifying context for a specific language. |
SlangIdentifying proper or improper use of slang. |
Success Stories
Mastercard’s R&D Labs needed unique, multi-lingual text data that covered 20 designated payment scenarios in English and Spanish, and they needed it fast.
Keeping a nation’s lights on means constantly inspecting electricity poles for damage. EDP partnered with DefinedCrowd to improve Asset Performance Management processes.
With the rise of voice technology, this leading global provider of audio equipment wanted to develop an automatic speech recognition (ASR) model.
A global electronics maker came to DefinedCrowd with the goal of building more inclusive facial recognition models, requiring accurately annotated images with highly specific criteria.
Smart companies see the pile of unstructured text floating through the digital realm as a strategic goldmine of consumer insights.
A Fortune 500 Tech company needed comprehensive speech training data in French that accounted for a wide range of dialects, requiring diverse data in terms of age, gender and regional dialects.
A visionary Fortune 500 Tech company leveraged sentiment analysis models to dig beyond surface-level understandings to extract granular-level insights.
