before_revision: Driven by the increasingly larger deep learning models, neural language generation (NLG) has enjoyed unprecedentedly improvement and is now able to generate a diversity of human-like texts on demand, granting itself the capability of serving as a human writing assistant. Text attribute transfer is one of the most important NLG tasks, which aims to control certain attributes that people may expect the texts to possess , such as sentiment, tense, emotion, political position, etc . It has a long history in Natural Language Processing but recently gains much more attention thanks to the promising performance brought by deep learning models. In this article , we present a systematic survey on these works for neural text attribute transfer. We collect all related academic works since the first appearance in 2017. We then select, summarize, discuss, and analyze around 65 representative works in a comprehensive way. Overall, we have covered the task formulation, existing datasets and metrics for model development and evaluation , and all methods developed over the last several years. We reveal that existing methods are indeed based on a combination of several loss functions with each of which serving a certain goal. Such a unique perspective we provide could shed light on the design of new methods. We conclude our survey with a discussion on open issues that need to be resolved for better future development .
after_revision: Text style transfer (TST) is an important task in natural language generation (NLG) , which aims to control certain attributes in the generated text , such as politeness, emotion, humor, and many others . It has a long history in the field of natural language processing (NLP), but recently it has gained significant attention thanks to the promising performance brought by deep learning models. In this paper , we present a systematic survey of the research done on neural text style transfer. We have collected, summarized, and discussed nearly 70 representative articles since the first neural text style transfer work in 2017. Overall, we have covered the task formulation, existing datasets and subtasks, evaluation metrics, and methods on parallel and non-parallel data. We also provide discussions a variety of important topics regarding TST, which can shed light on new development in this field. Our curated paper list is at URL
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edit_actions: collect all related academic works since the first appearance in
edit_actions: have collected, summarized, and discussed nearly 70 representative articles since the first neural text style transfer work in
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edit_actions: We then select, summarize, discuss, and analyze around 65 representative works in a comprehensive way.
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edit_actions: metrics for model development and evaluation , and all methods developed over the last several years. We reveal that existing methods are indeed based on a combination of several loss functions with each of which serving a certain goal. Such a unique perspective we provide could
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