University of California, Irvine finds only 40% of orgs track data on language service usage; less than half have a Language Access Coordinator. Limited budgets cited as hurdle.
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.
New research from IBM and UC San Diego explores synthetic parallel data as a means of pre-training machine translation models — with promising results.
Developers praise PyTorch, Facebook’s open source framework, for its flexibility, speed, and usability. But the machine translation world is not ready to dethrone TensorFlow just yet.
The world’s largest NLP conference, EMNLP, announces the winners of the 2019 awards for Best Paper, Best Paper Runner-Up, Best Demo Paper and Best Resource Paper.
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