Cambridge Researchers Tackle Neural Machine Translation’s Gender Bias
A research paper published in April 2020 explores how to reduce gender bias in neural machine translation (NMT) using transfer learning and fine-tuning.
SlatorCon Zurich is now SOLD OUT — See you on October 4th!
A research paper published in April 2020 explores how to reduce gender bias in neural machine translation (NMT) using transfer learning and fine-tuning.
Venture capitalists see opportunity as startups begin to apply breakthroughs in transfer learning and new language models, such as BERT, ELMo, OpenAI's GPT-2, and ULMFiT.
Francesco Bombassei, Senior Technical Program Manager at Google, presented at SlatorCon and discussed AutoML Translate, how ever accelerating computing power impacts development, and the human and technological challenges in machine learning.
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