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Found 6 papers, 4 papers with code
A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification
It is often difficult to reliably evaluate models which generate text.
RuPAWS: A Russian Adversarial Dataset for Paraphrase Identification
We compare it to the largest available dataset for Russian ParaPhraser and show that the best available paraphrase identifiers for the Russian language fail on the RuPAWS dataset.
ParaDetox: Detoxification with Parallel Data
To the best of our knowledge, these are the first parallel datasets for this task. We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources. We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches.
Don't lose the message while paraphrasing: A study on content preserving style transfer
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.
Studying the role of named entities for content preservation in text style transfer
Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer.
A Joint Approach to Compound Splitting and Idiomatic Compound Detection
Applications such as machine translation, speech recognition, and information retrieval require efficient handling of noun compounds as they are one of the possible sources for out-of-vocabulary (OOV) words.
