| Challenge: | Modern and post-modern free verse poems feature a large and complex variety in their prosodies that falls along a continuum from a more fluent to a disfluent and choppy style. |
| Approach: | They propose a method for automatic prosodic classification of spoken free verse poetry that integrates source text and audio and predicts the assigned class. |
| Outcome: | The proposed method can be validated on a large corpus of German author-read post-modern poetry and achieves a weighted f-measure of 0.73 when combining textual and phonetic evidence. |
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Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement (D18-1)
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| Challenge: | Automatic Chinese poetry generation is one of the first attempts towards computer writing. |
| Approach: | They propose a model which requires no supervised style labeling to generate stylistic poems . they incorporate mutual information, a concept in information theory, into modeling . |
| Outcome: | The proposed model generates stylistic poems without losing fluency and coherency . it is based on mutual information, a concept in information theory . |
Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets (2024.findings-emnlp)
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| Challenge: | a task evaluates how well LLMs recognize poetry, but performance varies by poetic form . performance varying by poetic forms; models struggle to identify unfixed poetic forms . |
| Approach: | They use a benchmark dataset to evaluate how well LLMs recognize poetry . they find that the models can identify fixed poetic forms with high accuracy . |
| Outcome: | The proposed task evaluates how well LLMs recognize poetry features . performance varies significantly by poetic form; models struggle to identify unfixed forms . authors urge more work that builds nuance and ambiguity into humanistic benchmarks . |
Evaluating Diversity in Automatic Poetry Generation (2024.emnlp-main)
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| Challenge: | Existing models for creative text generation are not evaluated regarding how different generated poems are from existing training sets. |
| Approach: | They evaluate the diversity of automatically generated poetry by comparing distributions of generated poetry to distributions in human poetry along structural, lexical, semantic and stylistic dimensions. |
| Outcome: | The proposed model types show that style-conditioning and character-level modeling increases diversity across virtually all dimensions. |
Automatic Poetry Generation from Prosaic Text (2020.acl-main)
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| Challenge: | In recent years, successful approaches have emerged to accurately model various aspects of natural language. |
| Approach: | They propose to combine neural networks with a poetry generation system that only uses standard text as input . they use standard text to model syntactic well-formedness and topical coherence . |
| Outcome: | The proposed framework is applied to the generation of poems in English and French . it uses standard, non-poetic text and its output is constrained to confer a poetic character . |
Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features (2021.eacl-main)
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| Challenge: | a prerequisite for the computational study of literature is the availability of properly digitized texts with reliable meta-data and ground-truth annotation. |
| Approach: | They propose to annotate prosodic features in large poetry corpora for English and German and train corpus driven neural models that enable large scale analysis. |
| Outcome: | The proposed models outperform baseline and BERT-based approaches in English and german and show that they learn foot boundaries better when jointly predicting syllable stress, aesthetic emotions and verse measures benefit from each other. |
ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language Models (2023.acl-long)
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| Challenge: | End-to-end models learn to complete a task by directly learning all steps, without intermediary algorithms such as hand-crafted rules or post-processing. |
| Approach: | They propose to train end-to-end poetry generation conditioned on styles such as rhyme, meter, and alliteration . they pre-train ByGPT5, a new token-free decoder-only language model, and fine-tune it on a custom corpus of English and German quatrains . |
| Outcome: | The proposed model outperforms other models on a large custom corpus of English and German quatrains while being more parameter efficient and performing favorably compared to humans. |
TopWORDS-Poetry: Simultaneous Text Segmentation and Word Discovery for Classical Chinese Poetry via Bayesian Inference (2023.emnlp-main)
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| Challenge: | Experimental studies confirm that TopWORDS-Poetry can successfully segment poetry words without pre-given vocabulary or training corpus. |
| Approach: | They propose an unsupervised method that can achieve reliable text segmentation and word discovery for classical Chinese poetry simultaneously without pre-given vocabulary or training corpus. |
| Outcome: | Experimental results show that TopWORDS-Poetry can segment poetry lines into meaningful words with high quality without pre-given vocabulary or training corpus. |
»textklang« – Towards a Multi-Modal Exploration Platform for German Poetry (2022.lrec-1)
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Nadja Schauffler, Toni Bernhart, Andre Blessing, Gunilla Eschenbach, Markus Gärtner, Kerstin Jung, Anna Kinder, Julia Koch, Sandra Richter, Gabriel Viehhauser, Ngoc Thang Vu, Lorenz Wesemann, Jonas Kuhn
| Challenge: | »textklang« aims to explore the relationship between written text and its potential and actual sonic realisation in lyric poetry . the platform will combine three modalities: the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of . musical setting of lyrical poetry. |
| Approach: | They propose to combine a multi-modal corpus of German lyric poetry from the Romantic era with a platform for systematic exploration. |
| Outcome: | The platform will combine the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of . a musical setting of lyric poetry. |
Don’t Go Far Off: An Empirical Study on Neural Poetry Translation (2021.emnlp-main)
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| Challenge: | despite improvements in machine translation quality, automatic poetry translation remains a challenging problem . et al., a study of automatic poetry translators shows that multilingual fine-tuning on poetic data outperforms bilingual fine-timing on non-poetic text . |
| Approach: | They propose to use poetic parallel corpora for 6 languages to study poetry translation . they find that multilingual fine-tuning on poetic data outperforms bilingual fine-uning . |
| Outcome: | The proposed model outperforms bilingual and multilingual models on poetic data . the proposed model is based on a parallel dataset of poetry translations for several languages . |
Integrating Disfluency-based and Prosodic Features with Acoustics in Automatic Fluency Evaluation of Spontaneous Speech (2020.lrec-1)
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| Challenge: | acoustics, prosody, and disfluency-based features are used to evaluate fluent/disfluent speech . filling pauses and word fragments are used for automatic fluency evaluation . |
| Approach: | They integrate acoustics, prosody, and disfluency-based features into an automatic fluency evaluation task. |
| Outcome: | The proposed model improves when integrated with prosodic features, but not when disfluent speech is detected. |