Automatic analysis of rhythmic poetry with applications to generation and translation

Authors:
Erica Greene;Tugba Bodrumlu;Kevin Knight
Affiliations:
Haverford College, Haverford, PA;Univ. of Southern California, Los Angeles, CA;Univ. of Southern California, Marina del Rey, CA
Venue:
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Year:
2010

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Abstract

We employ statistical methods to analyze, generate, and translate rhythmic poetry. We first apply unsupervised learning to reveal word-stress patterns in a corpus of raw poetry. We then use these word-stress patterns, in addition to rhyme and discourse models, to generate English love poetry. Finally, we translate Italian poetry into English, choosing target realizations that conform to desired rhythmic patterns.