Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features
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Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features Thomas Haider Department of Language and Literature, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main Institute for Natural Language Processing (IMS), University of Stuttgart [email protected] Abstract However, for projects that work with larger text corpora, close reading and extensive man- A prerequisite for the computational study of literature is the availability of properly digi- ual annotation are neither practical nor afford- tized texts, ideally with reliable meta-data and able. While the speech processing community ground-truth annotation. Poetry corpora do ex- explores end-to-end methods to detect and con- ist for a number of languages, but larger collec- trol the overall personal and emotional aspects of tions lack consistency and are encoded in vari- speech, including fine-grained features like pitch, ous standards, while annotated corpora are typ- tone, speech rate, cadence, and accent (Valle et al., ically constrained to a particular genre and/or 2020), applied linguists and digital humanists still were designed for the analysis of certain lin- rely on rule-based tools (Plecha´cˇ, 2020; Anttila guistic features (like rhyme). In this work, we provide large poetry corpora for English and Heuser, 2016; Kraxenberger and Menning- and German, and annotate prosodic features in haus, 2016), some with limited generality (Navarro- smaller corpora to train corpus driven neural Colorado, 2018; Navarro et al., 2016), or without models that enable robust large scale analysis. proper evaluation (Bobenhausen, 2011). Other ap- We show that BiLSTM-CRF models with syl- proaches to computational prosody make use of lable embeddings outperform a CRF baseline lexical resources with stress annotation, such as and different BERT-based approaches. In a the CMU dictionary (Hopkins and Kiela, 2017; multi-task setup, particular beneficial task re- Ghazvininejad et al., 2016), are based on words lations illustrate the inter-dependence of po- in prose rather than syllables in poetry (Talman etic features. A model learns foot boundaries et al., 2019; Nenkova et al., 2007), are in need of better when jointly predicting syllable stress, aesthetic emotions and verse measures benefit an aligned audio signal (Rosenberg, 2010;R osiger¨ from each other, and we find that caesuras are and Riester, 2015), or only model narrow domains quite dependent on syntax and also integral to such as iambic pentameter (Greene et al., 2010; shaping the overall measure of the line. Hopkins and Kiela, 2017; Lau et al., 2018) or Mid- dle High German (Estes and Hench, 2016). 1 Introduction To overcome the limitations of these approaches, Metrical verse, lyric as well as epic, was already we propose corpus driven neural models that model common in preliterate cultures (Beissinger, 2012), the prosodic features of syllables, and to evaluate and to this day the majority of poetry across against rhythmically diverse data, not only on sylla- the world is drafted in verse (Fabb and Halle, ble level, but also on line level. Additionally, even 2008). In order to reconstruct such oral traditions, though practically every culture has a rich heritage literary scholars mainly study textual resources of poetic writing, large comprehensive collections (rather than audio). The rhythmical analysis of of poetry are rare. We present in this work datasets poetic verse is still widely carried out by example- of annotated verse for a varied sample of around and theory-driven manual annotation of experts, 7000 lines for German and English. Moreover, we through so-called close reading (Carper and At- collect and automatically annotate large poetry cor- tridge, 2020; Kiparsky, 2020; Attridge, 2014; Men- pora for both languages to advance computational ninghaus et al., 2017). Fortunately, well-defined work on literature and rhythm. This may include constraints and the regularity of metrically bound the analysis and generation of poetry, but also more language aid the prosodic interpretation of poetry. general work on prosody, or even speech synthesis. 3715 Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 3715–3725 April 19 - 23, 2021. ©2021 Association for Computational Linguistics Our main contributions are: In English or German, the rhythm of a linguistic utterance is basically determined by the sequence 1. The collection and standardization of het- of syllable-related accent values (associated with erogenous text sources that span writing of pitch, duration and volume/loudness values) result- the last 400 years for both English and Ger- ing from the ‘natural’ pronunciation of a line, sen- man, together comprising over 5 million lines tence or text by a competent speaker who takes into of poetry. account the learned inherent word accents as well as syntax- and discourse-driven accents. Thus, lexi- 2. The annotation of prosodic features in a di- cal material comes with n-ary degrees of stress, de- verse sample of smaller corpora, including pending on morphological, syntactic, and informa- metrical and rhythmical features and the de- tion structural context. The prominence (or stress) velopment of regular expressions to determine of a syllable is thereby dependent on other syllables verse measure labels. in its vicinity, such that a syllable is pronounced 3. The development of preprocessing tools and relatively louder, higher pitched, or longer than its sequence tagging models to jointly learn our adjacent syllable. annotations in a multi-task setup, highlighting msr iambic.pentameter the relationships of poetic features. met - + | - + | - + | - +| - + | rhy 0 1 0 0 0 2 : 0 1 0 2 : My love is like to ice, and I to fire: 2 Manual Annotation msr iambic.tetrameter We annotate prosodic features in two small poetry met - + |- + | - + | - + | corpora that were previously collected and anno- rhy 0 1 0 2 0 0 0 1 : The winter evening settles down tated for aesthetic emotions by Haider et al.(2020). Both corpora cover a time period from around 1600 msr trochaic.tetrameter to 1930 CE, thus encompassing public domain liter- met + - | + - | + - | + rhy 0 0 1 0 1 0 2 : ature from the modern period. The English corpus Walk the deck my Captain lies, contains 64 poems with 1212 lines. The German corpus, after removing poems that do not permit a Figure 1: Examples of rhythmically annotated po- metrical analysis, contains 153 poems with 3489 etic lines, with meter (+/-), feet (|), main accents lines in total. Both corpora are annotated with some (2,1,0), caesuras (:), and verse measures (msr). Au- metadata such as the title of a poem and the name thors: Edmund Spenser, T.S. Eliot, and Walt Whitman. and dates of birth and death of its author. The Ger- man corpus further contains annotation on the year of publication and literary periods. 2.1 Annotation Workflow Figure1 illustrates our annotation layers with Prosodic annotation allows for a certain amount of three fairly common ways in which poetic lines freedom of interpretation and (contextual) ambi- can be arranged in modern English. A poetic line is guity, where several interpretations can be equally also typically called verse, from Lat. versus, origi- plausible. The eventual quality of annotated data nally meaning to turn a plow at the ends of succes- can rest on a multitude of factors, such as the extent sive furrows, which, by analogy, suggests lines of of training of annotators, the annotation environ- writing (Steele, 2012). ment, the choice of categories to annotate, and the In this work, we manually annotate the sequence personal preference of subjects (Mo et al., 2008; of syllables for metrical (meter, met) prominence Kakouros et al., 2016). (+/-), including a grouping of recurring metrical Three university students of linguistics/literature patterns, i.e., foot boundaries (|). We also oper- were involved in the manual annotation process. ationalize a more natural speech rhythm (rhy) by They annotated by silent reading of the poetry, annotating pauses in speech, caesuras (:), that seg- largely following an intuitive notion of speech ment the verse into rhythmic groups, and in these rhythm, as was the mode of operation in related groups we assign main accents (2), side accents (1) work (Estes and Hench, 2016). The annotators ad- and null accents (0). In addition, we develop a set ditionally incorporated philological knowledge to of regular expressions that derive the verse measure recognize instances of poetic license, i.e., knowing (msr) of a line from its raw metrical annotation. how the piece is supposed to be read. Especially 3716 the annotation accuracy of metrical syllable stress While 0s are quite unambiguous, it is not always and foot boundaries benefited from recognizing the clear when to set a primary (2) or side accent (1). schematic consistency of repeated verse measures, license through rhyme, or particular stanza forms. 2.2 Annotation Layers In this paper, we incorporate both a linguistic- systematic and a historically-intentional analysis (Mellmann, 2007), aiming at a systematic linguistic description of the prosodic features of poetic texts, but also using labels that are borrowed from histor- ically grown traditions to describe certain forms or patterns (such as verse measure labels). We evaluated our annotation by calculating Co- Figure 2: Confusion of German Main Accents hen’s Kappa between annotators. To capture dif- ferent granularities of correctness, we calculated Meter and Foot: In poetry, meter is the basic agreement on syllable level (accent/stress), be- prosodic structure of a verse. The underlying ab- tween syllables (for foot or caesura), and on full stract, and often top-down prescribed, meter con- lines (whether the entire line sequence is correct sists of a sequence of beat-bearing units (syllables) given a certain feature). that are either prominent or non-prominent.Non- prominent beats are attached to prominent ones to Main Accents & Caesuras: Caesuras are build metrical feet (e.g. iambic or trochaic ones).