Style Transfer in Text: Exploration and Evaluation

Abstract

Style transfer is an important problem in natural language processing (NLP). However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and principle evaluation metrics. In this paper, we propose to learn style transfer with non-parallel data. We explore two models to achieve this goal, and the key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. We also propose novel evaluation metrics which measure two aspects of style transfer: transfer strength and content preservation. We access our models and the evaluation metrics on two tasks: paper-news title transfer, and positive-negative review transfer. Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.

To appear in AAAI-18

References in corpus (8)

• ADADELTA: An Adaptive Learning Rate Method
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• Style Transfer from Non-Parallel Text by Cross-Alignment
• A Persona-Based Neural Conversation Model
• Preserving Color in Neural Artistic Style Transfer
• Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
• Demystifying Neural Style Transfer
• Adversarial Multi-Criteria Learning for Chinese Word Segmentation

Cited by in corpus (9)

• Multiple-Attribute Text Style Transfer
• Unsupervised Text Style Transfer using Language Models as Discriminators
• Evaluating prose style transfer with the Bible
• Zero-Shot Fine-Grained Style Transfer: Leveraging Distributed Continuous Style Representations to Transfer To Unseen Styles
• Language Style Transfer from Sentences with Arbitrary Unknown Styles
• Sentiment Transfer using Seq2Seq Adversarial Autoencoders
• Large-scale Hierarchical Alignment for Data-driven Text Rewriting
• Continual Learning for Text Classification with Information Disentanglement Based Regularization
• Multi-Pair Text Style Transfer on Unbalanced Data