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Found 7 papers, 5 papers with code
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company’s Reputation
We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness.
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.
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.
Text Detoxification using Large Pre-trained Neural Models
We compare our models with a number of methods for style transfer.
SkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span Detection
We show that fine-tuning a RoBERTa model for this problem is a strong baseline.
Methods for Detoxification of Texts for the Russian Language
We introduce the first study of automatic detoxification of Russian texts to combat offensive language.
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation
We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labeling a dataset for appropriateness.
