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

Understand

Instruction fine-tuned language models on a collection of instruction annotated datasets (FLAN) have shown highly effective to improve model performance and generalization to unseen tasks.

• However, a majority of standard parsing tasks including abstract meaning representation (AMR), universal dependency (UD), semantic role labeling (SRL) has been excluded from the FLAN collections for both model training and evaluations.
• In this paper, we take one of such instruction fine-tuned pre-trained language models, i.e.
• FLAN-T5, and fine-tune them for AMR parsing.