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Text style transfer techniques are gaining popularity in Natural LanguageProcessing, finding various applications such as text detoxification,sentiment, or formality transfer. However, the majority of the existingapproaches were tested on such domains as online communications on publicplatforms, music, or entertainment yet none of them were applied to the domainswhich are typical for task-oriented production systems, such as personal plansarrangements (e.g. booking of flights or reserving a table in a restaurant). Wefill this gap by studying formality transfer in this domain.  We noted that the texts in this domain are full of named entities, which arevery important for keeping the original sense of the text. Indeed, if forexample, someone communicates the destination city of a flight it must not bealtered. Thus, we concentrate on the role of named entities in contentpreservation for formality text style transfer.  We collect a new dataset for the evaluation of content similarity measures intext style transfer. It is taken from a corpus of task-oriented dialogues andcontains many important entities related to realistic requests that make thisdataset particularly useful for testing style transfer models before using themin production. Besides, we perform an error analysis of a pre-trained formalitytransfer model and introduce a simple technique to use information about namedentities to enhance the performance of baseline content similarity measuresused in text style transfer.

Studying the role of named entities for content preservation in text style transfer