Semi-supervised Text Style Transfer: Cross Projection in Latent Space
arXiv:1909.11493
Abstract
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data. In this paper, we first propose a semi-supervised text style transfer model that combines the small-scale parallel data with the large-scale nonparallel data. With these two types of training data, we introduce a projection function between the latent space of different styles and design two constraints to train it. We also introduce two other simple but effective semi-supervised methods to compare with. To evaluate the performance of the proposed methods, we build and release a novel style transfer dataset that alters sentences between the style of ancient Chinese poem and the modern Chinese.
arXiv commentEMNLP 2019
References in corpus (8)
- Sequence to Sequence Learning with Neural Networks
- A Neural Conversational Model
- Convolutional Neural Networks for Sentence Classification
- Dual Learning for Machine Translation
- Phrase-Based & Neural Unsupervised Machine Translation
- Style Transfer as Unsupervised Machine Translation
- Content preserving text generation with attribute controls
- Language Style Transfer from Sentences with Arbitrary Unknown Styles