GPT-too: A language-model-first approach for AMR-to-text generation
Table 2: Results on the LDC2017T10 development set. Rec(onstruction) uses the AMR reconstruction term (see §2) whereas Conditional does not.
['Approach', 'Decoding', 'BLEU', 'chrF++']
The model trained using this additional term achieves 30.41 BLEU and 61.36 chrF++, as opposed to 25.73 BLEU and 57.2 chrF++ without the term. We therefore use a reconstruction term training in the rest of the experiments. With beam size 10, we obtain 32.32 BLEU and 62.79 chrF++. With nucleus sampling at a cumulative probability mass of 0.9, performance drops to 28.75 BLEU and 61.19 chrF++. Finally, cycle-consistency re-ranking of the beam search outputs improves performance (33.57 BLEU, 64.86 chrF++) over the one best output.