Use quality estimation to prioritize translation activities and reduce translation costs
Identify high priority translation edits
Your project manager reports a high BLEU score and your client wants 200,000 words translated as quickly as possible. Which translated segments should your translators post-edit first? Prioritizing the right translations through segment quality estimation will yield the fastest possible project turn-around. KantanAnalytics™ allows you to identify which segments need to be prioritized.
Predictive Quality Estimation
High KantanAnalytic scores mean translations are more accurate and fluent – and require less post-editing effort. This reduces translation costs and accelerates translation delivery.
Prioritize translation activities
Quality estimation scores (KantanQES) uses advanced neural network modelling to predict the translation quality of every sentence. These predictive scores can be used to plan project schedules, determine project costs and assign suitable resources in order to deliver projects on-time and within budget.
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KantanAI works with some of the world’s largest organization to improve their products and services and deliver smoother customer journeys with A.I.
How it works
KantanAnalytics assigns a quality estimation score – expressed as a percentage – for each translation generated by a KantanMT engine. Segments with higher KantanAnalytic scores have proven to require considerably less post-editing, reducing post-editing time and cost!
Everything you need to drive your localization process for global growth
Translation adds complexity to a developer’s environment. The value of automated machine translation is that it takes the complexity out of translation. After the initial setup, you will be able to focus on what counts - making your products smarter and your customer experiences more exceptional.
KantanAnalytics creates a detailed project management report of all segments within a KantanMT project. This includes segment-by-segment quality estimation scores in addition to other useful project statistics such as word, character, placeholder and tag counts.