Month: January 2021 (Page 4 of 4)

Microsoft announced that DeBERTa now surpasses humans on the SuperGLUE benchmark. SuperGLUE is a challenging benchmarks for evaluating NLU models. The benchmark consists of a wide range of NLU tasks, including question answering, natural language inference, co-reference resolution, word sense disambiguation, and others. Top research teams around the world have been developing large-scale pretrained language models (PLMs) that have driven performance improvement on the SuperGLUE benchmark. Microsoft recently updated the DeBERTa model by training a larger version that consists of 48 Transformer layers with 1.5 billion parameters. The performance boost makes the single DeBERTa model surpass the human performance on SuperGLUE for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE benchmark rankings, outperforming the human baseline by a decent margin (90.3 versus 89.8). The model also sits at the top of the GLUE benchmark rankings with a macro-average score of 90.8.

Microsoft will release the 1.5-billion-parameter DeBERTa model and the source code to the public. In addition, DeBERTa is being integrated into the next version of the Microsoft Turing natural language representation model (Turing NLRv4). Our Turing models converge all language innovation across Microsoft, and they are then trained at large scale to support products like Bing, Office, Dynamics, and Azure Cognitive Services.

https://www.microsoft.com/en-us/research/blog/microsoft-deberta-surpasses-human-performance-on-the-superglue-benchmark/

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