Google Brain's XLNet bests BERT at 20 NLP tasks

A group of Google Brain and Carnegie Mellon University researchers this week introduced XLNet, an AI model capable of outperforming Google's cutting-edge BERT in 20 NLP tasks and achieving state-of-the-art results on 18 benchmark tasks. BERT (Bidirectional Encoder Representations from Transform) is Google's language representation model for unsupervised pretraining of NLP models first introduced last fall. XLNet achieved state-of-the-art performance in several tasks, including seven GLUE language understanding tasks, three reading comprehension tasks like SQuAD, and seven text classification tasks that include processing of Yelp and IMDB data sets. Text classification with XLNet saw a marked reduction of up to 16% in error rates compared to BERT. XLNet harnesses the best of autoregressive and autoencoding methods used for unsupervised pretraining through a variety of techniques detailed in an arXiv paper published Wednesday by a group of six authors.