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Stanford’s annual report on AI takes a high-level look at the industry, calls out machine translation fairness, and quotes Intento data on commercially available systems.
The EU Commission announced a EUR 20m tender for the development of advanced language technologies focused on natural language understanding and interaction.
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.
As machine translation gets better, the problem of bias — especially gender bias — remains a source of embarrassment for the industry. Why MT bias matters and how major players are trying to fix it.
Among the many things cooking at Meta: downloadable language packs for Android and iOS users to enjoy download-on-demand translation.
Google publishes translation dataset of biographies from Wikipedia in hopes of reducing gender bias in Google Translate output.
Machine translation company Intento uses metadata and MT-agnostic natural language processing to dodge AI biases around gender and tone of voice.
Researchers investigate the unexpected behavior of NMT and find surprising differences in the translations of extremely similar sentences.
Google Translate unveils a new approach to solving gender bias in machine translation, after its “99% reliable” 2018 model encountered problems and failed to scale.
A research paper published in April 2020 explores how to reduce gender bias in neural machine translation (NMT) using transfer learning and fine-tuning.
Google is concerned it might be mistakenly assuming genders in machine translation; initial effort to reduce gender bias only the first step in dealing with similar challenges in neural machine translation.
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