Few-Shot Natural Language Generation by Rewriting Templates
Table 7: Joint vs domain-specific (separate) NLG.
['[BOLD] Domain', '[BOLD] Separate  [BOLD] BLEU', '[BOLD] Separate  [BOLD] SER', '[BOLD] Joint  [BOLD] BLEU', '[BOLD] Joint  [BOLD] SER']
Because of these merits, we investigate the effect of joint modeling for NLG. The SGD-NLG dataset, which features a variety of domains, offers an excellent testbed for such a study. We focus on the 12 domains that are present in all 3 splits - train, dev and test. Concretely, we train a single model on domains and compare it with individual models trained for each domain separately. We notice consistent improvements in both metrics across all domains. The largest improvement is in the Movies domain, where BLEU improves by 10 points and SER reduces from 21.06 to just 0.15. On an average joint modeling improves BLEU by 3 points and reduces SER from 4% to just 0.53%, demonstrating successful knowledge sharing across domains.