Supervised Rhyme Detection with Siamese Recurrent Networks

Thomas Haider, Jonas Kuhn


Abstract
We present the first supervised approach to rhyme detection with Siamese Recurrent Networks (SRN) that offer near perfect performance (97% accuracy) with a single model on rhyme pairs for German, English and French, allowing future large scale analyses. SRNs learn a similarity metric on variable length character sequences that can be used as judgement on the distance of imperfect rhyme pairs and for binary classification. For training, we construct a diachronically balanced rhyme goldstandard of New High German (NHG) poetry. For further testing, we sample a second collection of NHG poetry and set of contemporary Hip-Hop lyrics, annotated for rhyme and assonance. We train several high-performing SRN models and evaluate them qualitatively on selected sonnetts.
Anthology ID:
W18-4509
Volume:
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico
Venues:
COLING | LaTeCH | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
81–86
Language:
URL:
https://aclanthology.org/W18-4509
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W18-4509.pdf