The proliferation of Artificial Intelligence and Machine Learning tools have professionals everywhere nervy about the future utility of their skill set. And when you take a peek at some of the exciting advances in these fields, it’s hard not to ask yourself “will I be replaced by a robot?”
Posts by Kyle Paice:
It’s not an everyday occurrence that translators and technology professionals come together and discuss the state of the language industry, but that’s exactly what happened last month in Santa Clara, CA. The event, The Future of Language Work: Enterprise, Technology, and Translation Professional Perspectives, was hosted by […]
Around 100 professionals from the language and technology industries came together in Santa Clara, CA last month to discuss the future of language work. The event, The Future of Language Work: Enterprise, Technology, and Translation Professional Perspectives, was hosted by translation startup, Lilt, and featured two panelist […]
In a recent case study with Zendesk, they talked to us about using a combination of human and machine translation to translate their large database of support content.
Many of us who have had the displeasure of post-editing a translation created by a machine would agree that the process is slow, tedious and out-of-style. However, there are always two sides of the story. So, we decided to ask our Twitter followers on their opinion of the post-editing process. The results? 47% of translators […]
Combining Machine Translation (MT) with auto-adaptive Machine Learning (ML) enables a new paradigm of machine assistance. Such systems learn from the experience, intelligence and insights of their human users, improving productivity by working in partnership, making suggestions and improving accuracy over time.
The net […]
Our new advanced termbase editor lets you manage terminology more effectively by keeping terms organized with meta information that you can customize.
Import terminology with meta fields or add your own fields. Your terms will appear in both the Lexicon and the Editor suggestions and help you increase consistency and quality.
Originally posted on LinkedIn by Greg Rosner.
I saw the phrase “linguistic janitorial work” in this Deloitte whitepaper on “AI-augmented government, using cognitive technologies to redesign public sector work”, used to describe the drudgery of translation work that so many translators are required to do today through […]
Abstract: We compare human translation performance in Lilt to SDL Trados, a widely used computer-aided translation tool. Lilt generates suggestions via an adaptive machine translation system, whereas SDL Trados relies primarily on translation memory. Five in-house English–French translators worked with each tool for an hour. […]
This post is an addendum to our original post on 1/10/2017 entitled 2017 Machine Translation Quality Evaluation.
We evaluate all machine translation systems for English-French and English-German. We report case-insensitive BLEU-4 , which is computed by the mteval scoring script from the Stanford […]