Simple finetuning of LLaMA 3.2 reaches SMATCH F1 0.804 on the AMR 3.0 test set, matching the APT+Silver parser and coming within 0.05 of the Graphene state of the art.
Online Back-Parsing for AMR-to-Text Generation
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abstract
AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph. Current research develops increasingly powerful graph encoders to better represent AMR graphs, with decoders based on standard language modeling being used to generate outputs. We propose a decoder that back predicts projected AMR graphs on the target sentence during text generation. As the result, our outputs can better preserve the input meaning than standard decoders. Experiments on two AMR benchmarks show the superiority of our model over the previous state-of-the-art system based on graph Transformer.
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Evaluation of Finetuned LLMs in AMR Parsing
Simple finetuning of LLaMA 3.2 reaches SMATCH F1 0.804 on the AMR 3.0 test set, matching the APT+Silver parser and coming within 0.05 of the Graphene state of the art.