Don't lose the message while paraphrasing: A study on content preserving
  style transfer
Nikolay Babakov David Dale Ilya Gusev Irina Krotova Alexander Panchenko
Text style transfer techniques are gaining popularity in natural language
processing allowing paraphrasing text in the required form: from toxic to
neural, from formal to informal, from old to the modern English language, etc.
Solving the task is not sufficient to generate some neural/informal/modern
text, but it is important to preserve the original content unchanged. This
requirement becomes even more critical in some applications such as style
transfer of goal-oriented dialogues where the factual information shall be kept
to preserve the original message, e.g. ordering a certain type of pizza to a
certain address at a certain time. The aspect of content preservation is
critical for real-world applications of style transfer studies, but it has
received little attention. To bridge this gap we perform a comparison of
various style transfer models on the example of the formality transfer domain.
To perform a study of the content preservation abilities of various style
transfer methods we create a parallel dataset of formal vs. informal
task-oriented dialogues. The key difference between our dataset and the
existing ones like GYAFC [17] is the presence of goal-oriented dialogues with
predefined semantic slots essential to be kept during paraphrasing, e.g. named
entities. This additional annotation allowed us to conduct a precise
comparative study of several state-of-the-art techniques for style transfer.
Another result of our study is a modification of the unsupervised method LEWIS
[19] which yields a substantial improvement over the original method and all
evaluated baselines on the proposed task.