Analysing the Focus of a Hierarchical Attention Network: the Importance of Enjambments When Classifying Post-modern Poetry

Abstract

After overcoming the traditional metrics, modern and postmodern poetry developed a large variety of 'free verse prosodies' that falls along a spectrum from a more fluent to a more disfluent and choppy style. We present a method, grounded in philological analysis and theories on cognitive (dis)fluency, to analyze this 'free verse spectrum' into six classes of poetic styles as well as to differentiate three types of poems with enjambments. We use a model for automatic prosodic analysis of spoken free verse poetry which uses deep hierarchical attention networks to integrate the source text and audio and predict the assigned class. We then analyze and fine-tune the model with a particular focus on enjambments and in two ways: we drill down on classification performance by analyzing whether the model focuses on similar traits of poems as humans would, specifically, whether it internally builds a notion of enjambment. We find that our model is similarly good as humans in finding enjambments; however, when we employ the model for classifying enjambment-dominated poem types, it does not pay particular attention to those lines. Adding enjambment labels to the training only marginally improves performance, indicating that all other lines are similarly informative for the model.

The Linguistic Structure of Haiku/Senryu and Its Evolution Over Time

“…Dalvean (2016) also used machine learning to classify canonical English poems from those that would be less likely to be anthologized. Bauman, Hussein and Meyer-Sickendiek (2018) employed deep hierarchical attention networks to categorize six classes of poetic styles in a sample of modern and post-modern free verse poems. McCurdy, Srikumar and Meyer (2015) created an open-source software called RhymeDesign.…”