Unsupervised Automatic Text Style Transfer Using LSTM

Abstract

In this paper, we focus on the problem of text style transfer which is considered as a subtask of paraphrasing. Most previous paraphrasing studies have focused on the replacements of words and phrases, which depend exclusively on the availability of parallel or pseudo-parallel corpora. However, existing methods can not transfer the style of text completely or be independent from pair-wise corpora. This paper presents a novel sequence-to-sequence (Seq2Seq) based deep neural network model, using two switches with tensor product to control the style transfer in the encoding and decoding processes. Since massive parallel corpora are usually unavailable, the switches enable the model to conduct unsupervised learning, which is an initial investigation into the task of text style transfer to the best of our knowledge. The results are analyzed quantitatively and qualitatively, showing that the model can deal with paraphrasing at different text style transfer levels.