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Instruction fine-tuned language models on a collection of instructionannotated datasets (FLAN) have shown highly effective to improve modelperformance and generalization to unseen tasks. However, a majority of standardparsing tasks including abstract meaning representation (AMR), universaldependency (UD), semantic role labeling (SRL) has been excluded from the FLANcollections for both model training and evaluations. In this paper, we take oneof such instruction fine-tuned pre-trained language models, i.e. FLAN-T5, andfine-tune them for AMR parsing. Our extensive experiments on various AMRparsing tasks including AMR2.0, AMR3.0 and BioAMR indicate that FLAN-T5fine-tuned models out-perform previous state-of-the-art models across alltasks. In addition, full fine-tuning followed by the parameter efficientfine-tuning, LoRA, further improves the model performances, setting newstate-of-the-arts in Smatch on AMR2.0 (86.4), AMR3.0 (84.9) and BioAMR (82.3).

AMR Parsing with Instruction Fine-tuned Pre-trained Language Models