GPT-too: A language-model-first approach for AMR-to-text generation
Table 1: Results on the LDC2017T10 development set using GPT-2 S(mall) and M(edium) with Rec(onstruction) loss (see §2) for different AMR representations (see §4).
['Model', 'Input', 'BLEU', 'chrF++']
Edge information, indicating relations between concepts, seems also to play a fundamental role since its absence strongly decreases performance in both DFS and PENMAN representations. Penman notation was chosen for the rest of the experiments.