Expert NLP translation services allow for major improvements in quality and therefore a wider reach.
As a machine translation or language data engineer, you want to be sure all your efforts lead to noticeable improvements in the quality of your output.
A balance of Natural Language Processing (NLP) translation and human touchpoints allows for major improvements in translation quality.
Multilingual Human Translation
Creating bilingual or multilingual datasets for machine learning requires native linguists to work in a controlled production environment.
To that end, we have an “intelligent” portal where clear job instructions are given to more than a thousand linguists doing the specialist, hands-on work.
There, expert linguists follow complex guidelines to create feature sets. Carefully recruited and trained, they work solely online on the platform. This guarantees the highest possible level of confidentiality and security.
Each step of the NLP production chain flows through here: data discovery and collection, text selection and cleaning, test set creation and validation of the NLP output.
MT Quality Evaluation
Linguists further evaluate and review your machine translation output, in or out of context.
As linguists search for patterns, annotate, label, and tag the data, the portal compares their work to the same job done by other linguists. They perform contrastive evaluations, offering additional observations in a structured, actionable way.
That way, we develop proof-based trust in the work our linguists do, and we can get them deeply involved in the language tweaks you need.
With quantitative or qualitative feedback, you can identify what improvements to focus your efforts on.
Expert NLP Translation: Why You Need Balance Between Machines and Humans
Although NLP led to huge advancements in language translation, AI translations aren’t quite perfect yet.
Machine translation can’t always understand cultural differences or translation context like human readers can.
For this reason, human oversight is still needed for accurately translating content from one language to another.
For example, we talk a lot about the need for high-quality data that you properly classify and label for advancements in artificial intelligence (AI). There’s a real balancing act here.
You want to keep costs down but get your innovation to market before the competition. So, you automate your annotation but risk missing out on the important human touchpoints that ensure quality and accuracy.
Instead, you also outsource the collection and labeling, running data through the gamut without clear direction, losing the essence of your project in the weeds.
Sometimes a client comes to us knowing exactly how they need to structure their data. However, the wide majority come to us with loose requirements. That’s either because their requirements are flexible, or they haven’t thought through all the possible variables yet.
As a language solutions provider, it’s our role to highlight all the ways we can customize your datasets while also steering you towards the most effective and price-conscious collection option for your solution.
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