Microsoft AI researchers today said they’ve created a Multi-Task Deep Neural Network (MT-DNN) that incorporates Google’s BERT AI to achieve state-of-the-art results. The MT-DNN was able to set new high performance standards in 7 of 9 NLP tasks from the General Language Understanding Evaluation (GLUE) benchmarks. The MT-DNN model, which also uses BERT, was first introduced by Microsoft AI researchers in January and also achieved state-of-the-art performance on several natural language tasks and set new GLUE benchmarks. The approach to achieve state-of-the-art results uses multi-task learning and a knowledge distillation method first introduced in 2015 by Google’s Geoffrey Hinton and AI chief Jeff Dean. Microsoft plans to open-source the MT-DNN model for learning text representations on GitHub in June, according to a blog post published today. The new distilled MT-DNN model saw better performance on GLUE tests than BERT and MT-DNN. “For each task, we train an ensemble of different MT-DNNs (teacher) that outperforms any single model, and then train a single MT-DNN (student) via multi-task learning to distill knowledge from these ensemble teachers,” reads a summary of the paper “Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding.” Bidirectional Encoder Representations from Transformers (BERT) was open-sourced by… Read full this story
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