Bad Data, Good Insights

Translators and Executives Need to Talk.

Last week, Bureau Works published some serious data about our context-sensitive translation technology. It was a rigorous study that evaluated over 4 million translated segments. Our engineering team works closely with our data scientists to produce good data that will be helpful in advancing our industry. They are scientists through and through, dissecting the data we collect for deep and nuanced insights.

I, on the other hand, am more of a data backhoe. I roll into a mine of potential information, scoop out a rough chunk, and see what I find inside. I do this by asking questions on LinkedIn. They are opinion questions, but opinions and emotions are their own type of data. They are data on where we are as a community, and they tell us what we need to develop in the relational part of the localization business.

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How machine learning transforms TM from knowledge to context

 

 In our fast-paced, technology-driven world, virtually every industry has experienced significant transformation. The field of translation is no exception, and it's on the cusp of a revolution that's reshaping the role of translators as we know it. In this blog post, we'll explore how machine learning is ushering in a new era, transforming Translation Memory (TM) from a repository of knowledge into a dynamic context-driven tool, and examine the profound implications this has for the future of translation.

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The Findings from the Translation Technology Insights 2020 survey and its implications for the industry

This is a guest post from one of ProZ.com's advertising partners, RWS Group.

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