Archives for Natural Language Understanding
GIT is pre-trained using the BERT encoder and KERMIT objective on an unsupervised LM task.
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Evaluation for many natural language understanding (NLU) tasks is broken.
The post Google’s Latest Guidelines To Build Better NLU Benchmarks appeared first on Analytics India Magazine.
Recently, Google Research open-sourced a platform for visualisation and understanding of natural language processing (NLP) models known as Language Interpretability Tool (LIT). LIT integrates local explanations, aggregate analysis and counterfactual generation into a streamlined, browser-based interface to enable rapid exploration and error analysis. Natural Language Processing technique has made several outstanding contributions in recent years.…
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Recently, researchers from DeepMind, UC Berkeley and the University of Oxford introduced a knowledge distillation strategy for injecting syntactic biases into BERT pre-training in order to benchmark natural language understanding. Bidirectional Encoder Representation from Transformers or BERT is one of the most popular neural network-based techniques for natural language processing (NLP) while pre-training. At the…
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Over the last few years, there have been significant advancements in the research of Chinese natural language understanding (NLU) and natural language processing (NLP). In order to make robust Chinese NLP models, various high-quality corpora of speech, text and others have been built by the researchers. The General Language Understanding Evaluation (GLUE) benchmark which was…
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