Learning Rhyming Constraints using Structured Adversaries
Table 1: Sampling efficiency: We obtain 10K samples of poetry without additional intervention during decoding, and report the expected samples as inverse of the fraction of samples satisfying valid rhyming patterns for the corresponding dataset. Lower values are better.
['Model', 'Expected #Samples Sonnet', 'Expected #Samples Limerick']
Note we do not report Deep-speare on Limerick due to their Sonnet specific assumption that for a given end-of-line word there is exactly one more rhyming word among other end-of-line words. Additionally, Rhyme-gan-ns performs worse than Rhyme-gan, and the difference in performance is more prominent in Limerick – demonstrating that the proposed structure in the discriminator provided useful inductive bias. Note that compared to 4 line quatrains in Sonnet, Limerick has 5 line poems and has arguably more complex rhyming pattern constraints.