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A new broad-based language model, trained specifically for certain tasks, balances training data to promote equal representation across language and improves accuracy up to 50%.
Microsoft and Amazon make similar new machine translation feature announcements within a day of each other, both intended for enterprise users.
To compete with other streaming services, Amazon has announced it will license its library of 4,000 films and 17,000 TV show episodes to foreign TV networks and other platforms.
Amazon releases MT-GenEval, a realistic dataset for evaluating gender bias in machine translation, to better understand how MT models perform on gender translation accuracy.
Generative AI hype, DeepL confirms unicorn round, translation scam activity in 2022, the best and worst performing listed LSPs, and Amazon’s study on human dubbing.
Unlike some fields in machine learning, machine translation still requires large sets of training data. The solution? Creating more data when none (or not enough) exists.
Amazon reviewed hundreds of hours of Prime content and found that human dubbers prioritize translation quality and speech naturalness over timing and lip sync.
Multilingual e-commerce searches introduce shoppers to new products; researchers identify language pairs with most to gain from launching or improving machine translation.
Researchers from Amazon present a simple procedure for extending pretrained machine translation evaluation metrics to the document level.
Amazon presents three ways it hopes to extend current natural language processing R&D beyond just a small subset of the world's 7,000+ languages.
Netflix ramps up gaming as shares fall; Amazon scales from 8 to 51 languages. In M&A, BIG acquires Lawlinguists, SeproTec buys tsd, and Transifex sells to PARC Partners.
As a leading tech site reports on the ‘dying art of subtitling,’ we unpack why it is anything but — and how LSPs, investors, and streaming giants are focused on subtitling like never before.
After an extraordinary year, our look back on 2021 — from Big Tech to big buys, machine translation to translators’ job prospects — shows the language industry is thriving.
Curating datasets, reviewing user-generated content, and liaising with locals — all in a day’s work for a linguist hired by big tech. (No computer science degree or published research required.)
We asked your opinions on English dubbing, MT research findings, post-editing TM versus MT output, and Google Translate.
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