Attention Is All You Need #15
Description
📋 논문의 정보를 알려주세요.
- Attention Is All You Need
- Ashish Vaswani et al
- Advances in Neural Information Processing of Systems 30 (NIPS 2017)
- NIPS 2017
📃 Abstract(본문)
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.
🔎 어떤 논문인지 소개해주세요.
- BERT나 GPT와 같은 핫한 NLP 모델들의 근간이 되는 Transformer에 논문입니다.
🔑 핵심 키워드를 적어주세요.
- Transformer, NLP