Learning Rhyming Constraints using Structured Adversaries
Table 3: Rhyming probe: We use the cosine similarity score of the learned representations to predict a word pair as rhyming or not, and report F1 score for this classification task. Rhym-em is an unsupervised rhyming pattern discovery method. grapheme-k baselines predict based on exact match of last k characters.
['Model', 'Sonnet', 'Limerick']
(threshold chosen to maximize f1 on dev split). We repeat the above analysis on the Limerick dataset and observe an F1 of 0.92 for Rhyme-gan. Deep-speare model reports F1 of 0.91 on Sonnet. As stated earlier, Deep-speare’s model is not amenable to Limerick - we do compare though with the max-margin classifier in Deep-speare model trained on Limerick which gets F1 score of 0.81. The scores are understandably lower since the AABBA pattern in limericks is not amenable to Sonnet specific assumptions made in Deep-speare model. On the other hand, Rhyme-gan achieves high F1 scores for both the datasets without incorporating any domain specific rhyming pattern information.