It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question a…

BERT is conceptually simple and empirically powerful. Jacob Devlin, et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" https://arxiv.org/abs/1810.04805 自然言語処理のあらゆるタスクに適用できる汎用的…

As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specif…

Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. Jacob Devlin, et al., "BERT: Pre-train…

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Jacob Devlin, et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding…