 Model description  AMRBART follows the BART model which uses a transformer encoder-encoder architecture. AMRBART is pre-trained with 6 tasks:  + learning to reconstruct the text based on the corrupted text. + learning to reconstruct AMR graphs based on the corrupted AMR graph. + learning to reconstruct the text based on the corrupted text and its corresponding AMR graph. + learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding text. + learning to reconstruct the text based on the corrupted text and its corresponding corrupted AMR graph. + learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding corrupted text.  AMRBART is particularly effective when fine-tuned for AMR parsing and AMR-to-text generation tasks.  