Zhenxin Fu
2019
Semi-supervised Text Style Transfer: Cross Projection in Latent Space
Mingyue Shang
|
Piji Li
|
Zhenxin Fu
|
Lidong Bing
|
Dongyan Zhao
|
Shuming Shi
|
Rui Yan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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.
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Co-authors
- Mingyue Shang 1
- Piji Li 1
- Lidong Bing 1
- Dongyan Zhao 1
- Shuming Shi 1
- show all...
- Rui Yan 1