Challenge: Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner.
Approach: They propose a corpus construction scheme that considers intent and formality of directives in Korean language.
Outcome: The proposed method is validated by a corpus construction scheme on Korean topics.

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Challenge: Typical datasets used for style transfer in NLP contain aligned pairs of two opposite extremes of a style.
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Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation (D19-55)

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Challenge: elucidates the dangerous current state of style transfer auto-evaluation research.
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Reformulating Unsupervised Style Transfer as Paraphrase Generation (2020.emnlp-main)

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Challenge: Existing systems for style transfer warp the input’s meaning through attribute transfer, which changes semantic properties such as sentiment.
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Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer (N18-1)

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Challenge: a lack of training and evaluation datasets, benchmarks and automatic metrics has blocked progress in this field.
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An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation (2020.coling-industry)

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Challenge: a limited amount of style data is needed for text style transfer, but there are no convincing methods for evaluating them.
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Discourse Component to Sentence (DC2S): An Efficient Human-Aided Construction of Paraphrase and Sentence Similarity Dataset (2020.lrec-1)

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Challenge: a dataset of similar sentences and paraphrases is a challenging task, but it requires high resources.
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Parallel Data Augmentation for Formality Style Transfer (2020.acl-main)

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Challenge: Formality style transfer is a task of automatically transforming text in one particular formality style into another.
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Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation (2023.acl-long)

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Challenge: Despite the growing number of Korean learners, little research has been conducted on Korean grammatical error correction (GEC) despite the difficulties of the Korean language, there is no evaluation benchmark for Korean GEC.
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Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer (D19-1)

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Challenge: Existing studies normalize informal sentences with rules, but they introduce noise if we use them in a naive way.
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Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer (2022.acl-short)

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Challenge: Text style transfer is a text generation task where a given sentence must be rewritten changing its style while preserving its meaning.
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