Style Transfer in Text: Exploration and Evaluation
Citations Over TimeTop 1% of 2018 papers
Abstract
The ability to transfer styles of texts or images, is an important measurement of the advancement of artificial intelligence (AI). 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 reliable evaluation metrics. In response to the challenge of lacking parallel data, we explore learning style transfer from non-parallel data. We propose two models to achieve this goal. The key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. Considering the problem of lacking principle evaluation metrics, we propose two novel evaluation metrics that measure two aspects of style transfer: transfer strength and content preservation. We benchmark our models and the evaluation metrics on two style transfer 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 similar content preservation score but higher style transfer strength comparing to auto-encoder.
Related Papers
- → A Benchmark Test Structure for Experimental Dynamic Substructuring(2011)9 cited
- → Solutions to the Third Benchmark Control Problem(1991)3 cited
- → The Performance Validation of Linear Programming Algorithm Based on Integrated Benchmark(2010)
- Theoretical Analysis of the Benchmark for Choosing Manipulative Instruments of Monetary Policies(2009)
- → Support Structure Performance Benchmark(2023)