Evaluating prose style transfer with the Bible
arXiv:1711.04731 · doi:10.1098/rsos.171920
Abstract
In the prose style transfer task a system, provided with text input and a target prose style, produces output which preserves the meaning of the input text but alters the style. These systems require parallel data for evaluation of results and usually make use of parallel data for training. Currently, there are few publicly available corpora for this task. In this work, we identify a high-quality source of aligned, stylistically distinct text in different versions of the Bible. We provide a standardized split, into training, development and testing data, of the public domain versions in our corpus. This corpus is highly parallel since many Bible versions are included. Sentences are aligned due to the presence of chapter and verse numbers within all versions of the text. In addition to the corpus, we present the results, as measured by the BLEU and PINC metrics, of several models trained on our data which can serve as baselines for future research. While we present these data as a style transfer corpus, we believe that it is of unmatched quality and may be useful for other natural language tasks as well.
References in corpus (11)
- Sequence to Sequence Learning with Neural Networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Recurrent Neural Network Regularization
- Pointer Sentinel Mixture Models
- Grammar as a Foreign Language
- Unsupervised Statistical Machine Translation
- Neural Paraphrase Generation with Stacked Residual LSTM Networks
- Unsupervised Machine Translation Using Monolingual Corpora Only
- Unsupervised Neural Machine Translation
- An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification
- Style Transfer in Text: Exploration and Evaluation
Cited by in corpus (6)
- Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
- Decomposing Textual Information For Style Transfer
- From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey
- Review of Text Style Transfer Based on Deep Learning
- HELFI: a Hebrew-Greek-Finnish Parallel Bible Corpus with Cross-Lingual Morpheme Alignment
- TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling