A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models
Table 5: Human correctness and quality judgments (%). Comparisons are better than (>), equal to (=), and worse than (
['Model', 'Correct. >', 'Correct. =', 'Correct. ', 'Quality >', 'Quality =', 'Quality ']
We find that the p1 model outputs are indistinguishable from the Slug model in terms of linguistic quality, regardless of the setting. In terms of correctness, the lexicalized p1 model is as good as or better than the Slug model 98% of the time. When using the delexicalized models, we don’t even need beam search. The delexicalized p1 greedy decoder is as good as or better than Slug 100% of the time.