We summarize our project Rhythmicalizer in which we analyze a corpus of post-modern poetry in a combination of qualitative hermeneutical and computational methods, as we have run the project over the course of the past three years (and preparing it for some time before that). Interdisciplinary work is always challenging and we here focus on some of the highlights of our collaboration.
http://www.rhythmicalizer.net
At least 80 % of modern and post-modern poems exhibit neither rhyme nor metrical schemes like iamb or trochee. However, does this mean that they are free of any rhythmical features? Of course not and the US American research on free verse prosody claims the opposite: Modern poets like Whitman, the Imagists, the Beat poets as well as contemporary Slam poets have developed a post-metrical idea of prosody, using rhythmical features of everyday language, prose, and musical styles like Jazz or Hip Hop, yielding a large and complex variety in their poetic prosodies which, however, appear to be much harder to quantify and regularize than traditional patterns.
In our joint project, we examine the largest portal for spoken poetry
Lyrikline1 and analyze and classify such rhythmical patterns in a human-in-the-loop
approach in which we interleave manual annotation with computational modelling
and data-based analysis [
The remainder of this paper is structured as follows: in the next section, we describe the research questions that we set out to address in our project; Copyright 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
This work is funded by the Volkswagen Foundation in the programme `Mixed Methods in the Humanities? Funding possibilities for the combination and the interaction of qualitative hermeneutical and digital methods' (funding codes 91926 and 93255). We wish to thank Hussein Hussein for his valuable contributions to the project. 1 http://lyrikline.org in Sections 3 and 4, we describe the methods used and the results obtained, respectively, and in Section 5 we describe { as much as is adequate in a public setting { the lessons taken during the course of the project. (It is still unclear, if all of these lessons also were lessons learned.) 2
Automating the analysis and di erentiating the prosodies of free verse
postmodern poetry comes with a multitude of challenges, including the manual
analysis and classi cation of su ciently many poems as training material for
an automated method. Yet, the amount of data that can be acquired (even in
the order of hundreds of poems) is still very little for modern machine learning
methods. However, the by far largest challenge of this endeavour lies in the nature
of free verse prosody, which is an almost purely spoken aspect of a poem and
is not readily observable from the pure textual form as in traditional metric
schemes (see, e.g., [
Our primary research questions are hence: (a) can we relate theories on the classi cation of the prosodies found in post-modern poetry to poems that we nd our collection of spoken poems, (b) can we pre-process the poems in our collections in such a way that they can be automatically analyzed, (c) can we build an automatic classi cation system that uses quantitative features derived from the poems to yield the classi cation despite the signi cant data sparsity, and (d) can we gather additional, new insight from the automatic classi cation in the humanities. 3
We here describe the setup of our project, in particular how we went about de ning and implementing the philological classi cation, as well as the processes used to prepare and automatically classify our data. Over the course of the project, we have extended and sometimes changed our methods, as is re ected in our publications, and we here only describe a subset of the methods used. 3.1
Theoretical Foundation: Grammetrical Ranking and Rhymic
Phrasing
Our classi cation of poetry is based on two theoretical approaches: 1) the Idea
of grammetrical ranking and 2) the Idea of rhythmic phrasing. 1) The term
grammetrics, coined by Donald Wesling, is a hybridization of grammar and
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metrics: the key hypothesis is that the interplay of sentence-structure and
linestructure can be accounted for more economically by simultaneous than by
successive analysis. [
2) The concept of rhythmic phrasing was developed by Richard Cureton. For
Cureton, rhythm embraces what has traditionally been regarded as very di erent
kinds of perceptual phenomena. Cureton divided rhythm - a global term covering
all relations of strength and weakness - into three distinct components, which he
terms meter, grouping, and prolongation. Meter involves the perception of beats in
regular patterns; grouping involves the apprehension of linguistic units organized
around a single peak of prominence; and prolongation involves the experience of
anticipation and arrival. Cureton claimed a hierarchical, multi-dimensional, and
preferential treatment of poetic rhythm, going back to [
As a result, the prosodic hierarchy to be considered for free verse is considerably more complex than for metric poetry and our working hypothesis of this hierarchy is depicted in Figure 1 (left). As can be seen, all levels of the linguistic hierarchy can carry poetic prosodic meaning, from the segment up to the periodic sentence. Based on this prosodic hierarchy, Figure 1 (right) depicts a categorization of some poets' works along two axes, the governing prosodic unit (x-axis) and the degree of iso/heterochronicity, or regularity of temporal arrangement (y-axis). The respective rhythm derives from the combined prosodic units and the degree of its isochronic (or heterochronic) succession. The green lines are meant as a localization of poets according to the interplay of grammetrical ranking and prosodic succession. The blue brackets mark the time span reduction (Rubato) as well as the prolongational reduction (Phrasing). According to the idea of time-span-reduction, the rubato is caused by a deviation from the isochronous rhythm. And according to the idea of prolongational reduction, the phrasing is divided into three di erent articulation techniques (legato, portato, staccato). 3.2
Manual Analysis and Annotation Based on our theoretical foundation, we devised a number of prosodic classes of poems and built a manually curated collection of poems for each class. Over the 32/143 course of our project, we extended the kinds of poems to di erentiate, starting with only various kinds of sound poetry (letristic vs. syllabic decompositions) via kinds of poems that di er by the way that enjambments are realized (variable foot poems vs. unemphasized enjambments vs. gestic rhythms) to a large collection of 18 classes. Presently, we work on identifying and specifying the relation of the classes to each other which will help inform the automatic classi cation methods described below. 3.3
Data Extraction and Preparation We work with the website Lyrikline which hosts a large collection of modern and post-modern readout poetry. Lyrikline was created by the Literaturwerkstatt Berlin and hosts contemporary international poetry as audio les (read by the authors) and texts (original versions & translations), so it o ers the melodies, sounds, and rhythms of international poetry, recited by the authors themselves. It covers more than 10,000 poems from about 1000 international poets from more than 60 di erent countries. Nearly 80 % of these are postmetrical poems. We focus our analysis on the roughly 2400 German-language poems on the page.2
To enable our analyses, we perform forced alignment [
attention RNN RNN ...
RNN RNN ...
RNN
RNN ... characters of poem line acoustic features of features of following poem line pause line representation . .
. line representation class softmax decision layer
In addition, we use various tools to extract syntactic information from the text as required. 3.4
Classi cation Methods We devised our project with primary concerns on data sparsity (i.e., the limitation that there is too little data to fully train advanced machine learning algorithms) as well as interpretability (i.e., the ability of a method to explain what aspects of the data determine its behaviour).
Thus, we rst focused our e orts on building interpretable classi ers (e.g., decision trees) on speci c interpretable features (e.g., the presence or absence of a verb in a line). However, based on the (relatively obvious) fact that the prosodic structure of a poem is re ected in most of its lines, and that our interpretable features need to be aggregated across the poem's lines anyway, we also build a hierarchical model for our poems, as depicted in Figure 2.
Our deep-learning based hierarchical attention model [
Over the course of our project, we have performed a number of experiments; we here only give a very rough overview of the results and refer the reader to the corresponding publications for further detail.
In [
We nd that pre-training and data augmentation are crucial for the success of neural learning techniques on the (relatively) small data sets typically found in digital humanities. Another way to success on di cult-to-describe material like sound poetry is the granularity of modelling: instead of using words as the textual unit of processing, our recurrent networks use the sequence of individual characters found in the poem. This has (at least) the following two advantages: there is a xed set of characters (as compared to the endless possibilities for words) which means that our models has far fewer parameters to train and cannot be confronted with `out-of-vocabulary words' when applied to a new poem. Secondly, the concept of `word' does not necessarily re ect what we nd in sound poetry and the prosodic features are actually very well re ected by the stream of characters (e.g., the recurrence of consonant-vowel pairs) as compared to words.
Finally, and coming back to our original ideas [
Over the course of our interdisciplinary project, the common research question has been a strong uniting factor given the sometimes di ering research interests involved (creating classi cation systems that work despite data sparsity vs. the interest in nding evidence in data for humanistic theories). A strong enabling factor in the joint work was the ability to openly ask about the other eld's theory, applications, limitations, and to not be shy to ask again if the given answer was unclear. It often helped to agree to disagree and to move on despite of this, or to compromise on what should be done. 35/143
The project sometimes was hindered by technical limitations, such as forced alignment of text to speech (or syntactic analysis) breaking down for sound poetry, and often being too inaccurate (or with too low coverage) for large amounts of the material. This meant that a great deal of manual annotation of the base material had to be performed, despite the later stages being successfully automated. This means that the classi ers produced in the project cannot always be applied to new data in a fully automatic way and the work required to prepare data (although relatively straightforward) can be more than would be required for a manual prosodic analysis itself.