BART

2000년 도입 · 논문 1,642편에서 사용

BART is a denoising autoencoder for pretraining sequence-to-sequence models. It is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Transformer-based neural machine translation architecture. It uses a standard seq2seq/NMT architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT). This means the encoder's attention mask is fully visible, like BERT, and the decoder's attention mask is causal, like GPT2.

출처: BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

소개 논문: BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension