Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
Table 8: E2E dataset diversity metrics. Rows in gray correspond to metrics that we cite from Dušek et al. (2019).
['[BOLD] Model', '[BOLD] Unique tokens', '[BOLD] Unique trigrams', '[BOLD] Shannon entropy', '[BOLD] Cond. entropy bigrams']
We report the diversity metrics used for evaluating E2E challenge submissions in Dušek et al. All of the models with enriched semantic representations — S2S-Token, S2S-Tree, and S2S-Constr — show higher diversity than neural baselines without diversity considerations. Combined with our improved BLEU scores, this seems to indicate that adding discourse relation information to input MRs can increase diversity, without incurring losses on automatic metrics (as is the case with the diversity-promoting ADAPT system).