evlin, JacobChang, Ming‑WeiLee, KentonToutanova, Kristina2025-06-022025-06-022018-10-11https://doi.org/10.48550/arXiv.1706.03762http://data.inu.ac.kr/handle/123456789/1952BERT는 양방향 Transformer 인코더 구조를 기반으로 사전 학습(pretraining)된 언어모델로, 다양한 NLP 태스크에서 뛰어난 성능을 보여주며 언어 이해의 새로운 패러다임을 제시했습니다. ©2018 Google AI LanguageWe 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).en-USArticle