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Online Back-Parsing for AMR-to-Text Generation

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arxiv 2010.04520 v1 pith:G7XYC7LL submitted 2020-10-09 cs.CL

Xuefeng Bai , Linfeng Song , Yue Zhang This is my paper
classification cs.CL
keywords generationgraphamr-to-textbetterdecodersgraphsinputmeaning
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

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  1. Evaluation of Finetuned LLMs in AMR Parsing

    cs.CL 2025-08 conditional novelty 4.0 of 10

    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.

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