Pre-training of Deep Bidirectional Transformers for Language Understanding
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Date
2018-10-11
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Publisher
arXiv
Abstract
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. 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. 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-specific architecture modifications.
BERT is conceptually simple and empirically powerful. 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 answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
Description
BERT는 양방향 Transformer 인코더 구조를 기반으로 사전 학습(pretraining)된 언어모델로, 다양한 NLP 태스크에서 뛰어난 성능을 보여주며 언어 이해의 새로운 패러다임을 제시했습니다.
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