Learning Rhyming Constraints using Structured Adversaries
Table 2: Held out negative log likelihood per token for poems in test split.
['Model', 'NLL Sonnet', 'NLL Limerick']
Likelihood on held out data We report negative log likelihood (NLL) on test splits For Sonnet, Rhyme-gan achieves a test set NLL of 3.98. Our model without adversarial learning i.e. Rhyme-lm, achieves a test set NLL of 3.97. Deep-speare reports a test set NLL of 4.38. Note that our language model is hierarchical while Deep-speare has a linear model. The NLL for Rhyme-lm and Rhyme-gan are very similar, though Rhyme-gan gets much better sampling efficiency scores than Rhyme-lm.