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Archive by tag: Machine translationReturn

When No Doesn’t Mean “No”

The word No seems to have been going around a lot in conversations lately. From discussions (welfare, personal space, the right to say No) to a conversation with my local taxi driver about the use of the word “Non” as a simple statement in France, when he asked about purchasing something, and where he perceived not just the word used as a negative, but as the expression of a cultural difference. “Non” with a Gallic shrug can mean so much more than a simple “No, we don’t have any”; it refl...
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The Coming Content Cataclysm

Multimedia, transcribed audio, and AI-generated content in all the world’s digital languages join more traditional content types in filling up data centers. Together they create challenges and opportunities across organizations, raise the alarm for more oversight of content, and further the case for aligning enterprise content strategies, investment, and operations.
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“Misusing” Large Language Models and the Future of MT

Large language models have been in the news a lot in November and December and the coverage has been mixed, to put it mildly. Meta posted its Galactica model on November 15 but took it down just three days later in the face of intense criticism. By contrast, when OpenAI released ChatGPT two weeks later, on November 30, the response was much more positive. Examining why the reactions were so different provides insight into the potential and limitations of machine translation (MT) as well as cauti...
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Curves Ahead: MT and the Future of Language Technology

Technology developments tend to follow a typical pattern of improvement over time, known as an S-curve. Although it is a familiar pattern, it is worth unpacking its five phases and considering how they apply to language technology and forecasts about it. Examining how they have played out with successive generations of machine translation points to a future in which other advanced natural language processing technologies have tremendous potential to deliver useful and innovative capabilities.
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Sentient AI: Parrot, Parity, or Parody?

Last week, the Washington Post published an article about Blake Lemoine’s claim that his employer Google’s LaMDA language model/chatbot system had achieved sentience and had a “soul.” Lemoine, an engineer in the company’s responsible AI group, based his assertion on a dialogue in which LaMDA expressed human-seeming sentiments and concepts. Google placed Lemoine on leave, thereby sparking renewed discussion about what machine sentience is and what it means. What can the experience of the lan...
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Airbnb: A Lesson in How to Implement Language at the Platform Level

Are you ready to implement language as a feature at the platform level? Do you know how to gain executive approval for the business case to achieve that? Do you even know what I’m talking about?! Read on to find out how Airbnb did it and the questions to ask your team to find out if you’re ready to embark on the same journey.
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Responsive Machine Translation: The Next Frontier for MT

CSA Research’s recent survey-based examinations of machine translation deployment at language service providers, enterprises, government agencies, and among freelancers revealed an ever-widening engagement with the technology. Although it didn’t surprise us, we also found widespread skepticism of claims that MT has reached human parity with numerous calls in open-ended survey comments for “truth in advertising.” Just as significantly, we saw widespread desire for MT to be more suitable for t...
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2020: A Year of Superlatives in the Language Industry

CSA Research recently released our list of the 100 largest LSPs and langtech providers, along with eight regional lists that add more than 80 firms. The rankings are based on our annual survey of more than 450 companies around the globe with revenue and business data supplied by the companies themselves and validated by their executives. Here’s a map of CSA Research’s 10 largest LSPs for 2021 – follow the lines to see the journeys of these companies over 17 years to their current positions. B...
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Building a Comprehensive View of Machine Translation’s Potential

It is no secret that machine translation (MT) has gone from a relatively niche solution to seeming ubiquity in just a few short years. A forthcoming report from CSA Research on MT use at language services providers shows a 51% increase in adoption since 2019, with over two-thirds of LSPs now using it openly. Similarly, cloud TMS developers reported that 2020 was the year when the majority of segments that they process involved MT. To help develop a comprehensive view of the technology’s role in...
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Making the Best of a Bad Year: Five Lessons for 2021

As we look back at the annus horribilis that was 2020, what are some things we can learn and take forward to improve 2021 and come back stronger than ever? In this post, I summarize some of the lessons CSA Research has learned from our conversations with professionals working in enterprise localization departments, at LSPs, or as freelance linguists.
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