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

arXiv: https://arxiv.org/abs/2206.09676 · HTML: https://mlsanity.com/paper/2206.09676/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer](https://mlsanity.com/paper/1803.06535/) (2018)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://mlsanity.com/paper/2010.05700/) (2020)
- [Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models](https://mlsanity.com/paper/1707.01161/) (2017)
- [Towards a Better Metric for Evaluating Question Generation Systems](https://mlsanity.com/paper/1808.10192/) (2018)
- [Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen](https://mlsanity.com/paper/2005.00701/) (2020)
- [TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling](https://mlsanity.com/paper/2010.03802/) (2020)
- [Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer](https://mlsanity.com/paper/2105.06947/) (2021)
- [Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer](https://mlsanity.com/paper/2110.10668/) (2021)
- [Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric](https://mlsanity.com/paper/2004.05001/) (2020)
- [Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization](https://mlsanity.com/paper/2108.00449/) (2021)
- [A Review of Human Evaluation for Style Transfer](https://mlsanity.com/paper/2106.04747/) (2021)

## Built on by (most cited first, 1 of 1)

- [Don't lose the message while paraphrasing: A study on content preserving style transfer](https://mlsanity.com/paper/2308.09055/) (2023)

## Abstract (arXiv)

Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer. However, the majority of the existing approaches were tested on such domains as online communications on public platforms, music, or entertainment yet none of them were applied to the domains which are typical for task-oriented production systems, such as personal plans arrangements (e.g. booking of flights or reserving a table in a restaurant). We fill this gap by studying formality transfer in this domain. We noted that the texts in this domain are full of named entities, which are very important for keeping the original sense of the text. Indeed, if for example, someone communicates the destination city of a flight it must not be altered. Thus, we concentrate on the role of named entities in content preservation for formality text style transfer. We collect a new dataset for the evaluation of content similarity measures in text style transfer. It is taken from a corpus of task-oriented dialogues and contains many important entities related to realistic requests that make this dataset particularly useful for testing style transfer models before using them in production. Besides, we perform an error analysis of a pre-trained formality transfer model and introduce a simple technique to use information about named entities to enhance the performance of baseline content similarity measures used in text style transfer.