Evaluating prose style transfer with the Bible
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.
Code (1)
Tasks
Style TransferSimilar Papers 제목 키워드 기반
Prose for a Painting
Painting captions are often dry and simplistic which motivates us to describe a painting creatively in the style of Shakespearean prose. This is a difficult problem, since there does not exist a large supervised dataset …
Style TransferStyle or Content? Evaluating Style Classifiers with Controlled Content Overlap
Style classifiers can use content cues that correlate with style labels in naturally collected data, yet we lack a systematic way to measure this reliance. We study this problem with a controlled content overlap setup bu…
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation
The topic of this paper is neural multi-task training for text style transfer. We present an efficient method for neutral-to-style transformation using the transformer framework. We demonstrate how to prepare a robust mo…
Multi-Task LearningParaphrase GenerationStyle TransferText Style TransferMassive Styles Transfer with Limited Labeled Data
Language style transfer has attracted more and more attention in the past few years. Recent researches focus on improving neural models targeting at transferring from one style to the other with labeled data. However, tr…
DenoisingStyle TransferTranslationFinding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering
Question answering (QA) has significantly benefitted from deep learning techniques in recent years. However, domain-specific QA remains a challenge due to the significant amount of data required to train a neural network…
Domain AdaptationQuestion AnsweringSentenceTransfer Learning+1