% Please download the latest anthology.bib from
%
% http://aclweb.org/anthology/anthology.bib.gz

@inproceedings{lai-etal-2022-human,
    title = "Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer",
    author = "Lai, Huiyuan  and
      Mao, Jiali  and
      Toral, Antonio  and
      Nissim, Malvina",
    booktitle = "Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.humeval-1.9",
    doi = "10.18653/v1/2022.humeval-1.9",
    pages = "102--115",
    abstract = "Although text style transfer has witnessed rapid development in recent years, there is as yet no established standard for evaluation, which is performed using several automatic metrics, lacking the possibility of always resorting to human judgement. We focus on the task of formality transfer, and on the three aspects that are usually evaluated: style strength, content preservation, and fluency. To cast light on how such aspects are assessed by common and new metrics, we run a human-based evaluation and perform a rich correlation analysis. We are then able to offer some recommendations on the use of such metrics in formality transfer, also with an eye to their generalisability (or not) to related tasks.",
}

@inproceedings{logacheva-etal-2022-study,
    title = "A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification",
    author = "Logacheva, Varvara  and
      Dementieva, Daryna  and
      Krotova, Irina  and
      Fenogenova, Alena  and
      Nikishina, Irina  and
      Shavrina, Tatiana  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.humeval-1.8",
    doi = "10.18653/v1/2022.humeval-1.8",
    pages = "90--101",
    abstract = "It is often difficult to reliably evaluate models which generate text. Among them, text style transfer is a particularly difficult to evaluate, because its success depends on a number of parameters.We conduct an evaluation of a large number of models on a detoxification task. We explore the relations between the manual and automatic metrics and find that there is only weak correlation between them, which is dependent on the type of model which generated text. Automatic metrics tend to be less reliable for better-performing models. However, our findings suggest that, ChrF and BertScore metrics can be used as a proxy for human evaluation of text detoxification to some extent.",
}

@inproceedings{DBLP:conf/aaai/YamshchikovSKT21,
  author    = {Ivan P. Yamshchikov and
               Viacheslav Shibaev and
               Nikolay Khlebnikov and
               Alexey Tikhonov},
  title     = {Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity
               Metric},
  booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}
               2021, Thirty-Third Conference on Innovative Applications of Artificial
               Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances
               in Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9,
               2021},
  pages     = {14213--14220},
  publisher = {{AAAI} Press},
  year      = {2021},
  url       = {https://ojs.aaai.org/index.php/AAAI/article/view/17672},
  timestamp = {Mon, 07 Jun 2021 11:46:04 +0200},
  biburl    = {https://dblp.org/rec/conf/aaai/YamshchikovSKT21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{moskovskiy-etal-2022-exploring,
    title = "Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models.",
    author = "Moskovskiy, Daniil  and
      Dementieva, Daryna  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-srw.26",
    doi = "10.18653/v1/2022.acl-srw.26",
    pages = "346--354",
    abstract = "Detoxification is a task of generating text in polite style while preserving meaning and fluency of the original toxic text. Existing detoxification methods are monolingual i.e. designed to work in one exact language. This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models in this setting. Unlike previous works we aim to make large language models able to perform detoxification without direct fine-tuning in a given language. Experiments show that multilingual models are capable of performing multilingual style transfer. However, tested state-of-the-art models are not able to perform cross-lingual detoxification and direct fine-tuning on exact language is currently inevitable and motivating the need of further research in this direction.",
}

@article{DBLP:journals/mti/DementievaMLDKS21,
  author    = {Daryna Dementieva and
               Daniil Moskovskiy and
               Varvara Logacheva and
               David Dale and
               Olga Kozlova and
               Nikita Semenov and
               Alexander Panchenko},
  title     = {Methods for Detoxification of Texts for the Russian Language},
  journal   = {Multimodal Technol. Interact.},
  volume    = {5},
  number    = {9},
  pages     = {54},
  year      = {2021},
  url       = {https://doi.org/10.3390/mti5090054},
  doi       = {10.3390/mti5090054},
  timestamp = {Wed, 15 Dec 2021 10:31:28 +0100},
  biburl    = {https://dblp.org/rec/journals/mti/DementievaMLDKS21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{logacheva-etal-2022-paradetox,
    title = "{P}ara{D}etox: Detoxification with Parallel Data",
    author = "Logacheva, Varvara  and
      Dementieva, Daryna  and
      Ustyantsev, Sergey  and
      Moskovskiy, Daniil  and
      Dale, David  and
      Krotova, Irina  and
      Semenov, Nikita  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.469",
    doi = "10.18653/v1/2022.acl-long.469",
    pages = "6804--6818",
    abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. 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. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.",
}

@article{roberta,
  author    = {Yinhan Liu and
               Myle Ott and
               Naman Goyal and
               Jingfei Du and
               Mandar Joshi and
               Danqi Chen and
               Omer Levy and
               Mike Lewis and
               Luke Zettlemoyer and
               Veselin Stoyanov},
  title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
  journal   = {CoRR},
  volume    = {abs/1907.11692},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.11692},
  archivePrefix = {arXiv},
  eprint    = {1907.11692},
  timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{jigsaw_toxic,
  author = {Jigsaw},
  year = {2018},
  title = {Toxic Comment Classification Challenge},
  howpublished = {\href{https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge}{https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge}},
  note = {Accessed: 2021-03-01}
}

@inproceedings{wieting-etal-2019-beyond,
    title = "Beyond {BLEU}:Training Neural Machine Translation with Semantic Similarity",
    author = "Wieting, John  and
      Berg-Kirkpatrick, Taylor  and
      Gimpel, Kevin  and
      Neubig, Graham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1427",
    doi = "10.18653/v1/P19-1427",
    pages = "4344--4355",
    abstract = "While most neural machine translation (NMT)systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can significantly improve final translation accuracy. However, training with BLEU has some limitations: it doesn{'}t assign partial credit, it has a limited range of output values, and it can penalize semantically correct hypotheses if they differ lexically from the reference. In this paper, we introduce an alternative reward function for optimizing NMT systems that is based on recent work in semantic similarity. We evaluate on four disparate languages trans-lated to English, and find that training with our proposed metric results in better translations as evaluated by BLEU, semantic similarity, and human evaluation, and also that the optimization procedure converges faster. Analysis suggests that this is because the proposed metric is more conducive to optimization, assigning partial credit and providing more diversity in scores than BLEU",
}

@inproceedings{wieting-gimpel-2018-paranmt,
    title = "{P}ara{NMT}-50{M}: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations",
    author = "Wieting, John  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1042",
    doi = "10.18653/v1/P18-1042",
    pages = "451--462",
    abstract = "We describe ParaNMT-50M, a dataset of more than 50 million English-English sentential paraphrase pairs. We generated the pairs automatically by using neural machine translation to translate the non-English side of a large parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M can be a valuable resource for paraphrase generation and can provide a rich source of semantic knowledge to improve downstream natural language understanding tasks. To show its utility, we use ParaNMT-50M to train paraphrastic sentence embeddings that outperform all supervised systems on every SemEval semantic textual similarity competition, in addition to showing how it can be used for paraphrase generation.",
}

@article{cola,
  author    = {Alex Warstadt and
               Amanpreet Singh and
               Samuel R. Bowman},
  title     = {Neural Network Acceptability Judgments},
  journal   = {Trans. Assoc. Comput. Linguistics},
  volume    = {7},
  pages     = {625--641},
  year      = {2019},
  url       = {https://transacl.org/ojs/index.php/tacl/article/view/1710},
  timestamp = {Wed, 17 Feb 2021 21:55:24 +0100},
  biburl    = {https://dblp.org/rec/journals/tacl/WarstadtSB19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{sellam-etal-2020-bleurt,
    title = "{BLEURT}: Learning Robust Metrics for Text Generation",
    author = "Sellam, Thibault  and
      Das, Dipanjan  and
      Parikh, Ankur",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.704",
    doi = "10.18653/v1/2020.acl-main.704",
    pages = "7881--7892",
    abstract = "Text generation has made significant advances in the last few years. Yet, evaluation metrics have lagged behind, as the most popular choices (e.g., BLEU and ROUGE) may correlate poorly with human judgment. We propose BLEURT, a learned evaluation metric for English based on BERT. BLEURT can model human judgment with a few thousand possibly biased training examples. A key aspect of our approach is a novel pre-training scheme that uses millions of synthetic examples to help the model generalize. BLEURT provides state-of-the-art results on the last three years of the WMT Metrics shared task and the WebNLG data set. In contrast to a vanilla BERT-based approach, it yields superior results even when the training data is scarce and out-of-distribution.",
}

@article{kuratov2019adaptation,
  author    = {Yuri Kuratov and
               Mikhail Arkhipov},
  title     = {Adaptation of Deep Bidirectional Multilingual Transformers for Russian
               Language},
  journal   = {CoRR},
  volume    = {abs/1905.07213},
  year      = {2019},
  archivePrefix = {arXiv},
  eprint    = {1905.07213},
  timestamp = {Tue, 28 May 2019 12:48:08 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1905-07213.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{feng2020languageagnostic,
  author    = {Fangxiaoyu Feng and
               Yinfei Yang and
               Daniel Cer and
               Naveen Arivazhagan and
               Wei Wang},
  editor    = {Smaranda Muresan and
               Preslav Nakov and
               Aline Villavicencio},
  title     = {Language-agnostic {BERT} Sentence Embedding},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational
               Linguistics (Volume 1: Long Papers), {ACL} 2022, Dublin, Ireland,
               May 22-27, 2022},
  pages     = {878--891},
  publisher = {Association for Computational Linguistics},
  year      = {2022},
  url       = {https://doi.org/10.18653/v1/2022.acl-long.62},
  doi       = {10.18653/v1/2022.acl-long.62},
  timestamp = {Mon, 01 Aug 2022 16:27:43 +0200},
  biburl    = {https://dblp.org/rec/conf/acl/FengYCA022.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1423",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).",
}

@article{DBLP:journals/corr/abs-2204-13638,
  author    = {Ilya Gusev},
  title     = {Russian Texts Detoxification with Levenshtein Editing},
  journal   = {CoRR},
  volume    = {abs/2204.13638},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2204.13638},
  doi       = {10.48550/arXiv.2204.13638},
  eprinttype = {arXiv},
  eprint    = {2204.13638},
  timestamp = {Mon, 02 May 2022 17:29:09 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2204-13638.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2210-12814,
  author    = {Vladislav Mikhailov and
               Tatiana Shamardina and
               Max Ryabinin and
               Alena Pestova and
               Ivan Smurov and
               Ekaterina Artemova},
  title     = {RuCoLA: Russian Corpus of Linguistic Acceptability},
  journal   = {CoRR},
  volume    = {abs/2210.12814},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2210.12814},
  doi       = {10.48550/arXiv.2210.12814},
  eprinttype = {arXiv},
  eprint    = {2210.12814},
  timestamp = {Fri, 28 Oct 2022 14:21:57 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2210-12814.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{gudkov-etal-2020-automatically,
    title = "Automatically Ranked {R}ussian Paraphrase Corpus for Text Generation",
    author = "Gudkov, Vadim  and
      Mitrofanova, Olga  and
      Filippskikh, Elizaveta",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.ngt-1.6",
    doi = "10.18653/v1/2020.ngt-1.6",
    pages = "54--59",
    abstract = "The article is focused on automatic development and ranking of a large corpus for Russian paraphrase generation which proves to be the first corpus of such type in Russian computational linguistics. Existing manually annotated paraphrase datasets for Russian are limited to small-sized ParaPhraser corpus and ParaPlag which are suitable for a set of NLP tasks, such as paraphrase and plagiarism detection, sentence similarity and relatedness estimation, etc. Due to size restrictions, these datasets can hardly be applied in end-to-end text generation solutions. Meanwhile, paraphrase generation requires a large amount of training data. In our study we propose a solution to the problem: we collect, rank and evaluate a new publicly available headline paraphrase corpus (ParaPhraser Plus), and then perform text generation experiments with manual evaluation on automatically ranked corpora using the Universal Transformer architecture.",
}

@inproceedings{DBLP:conf/lrec/MartynovKLPKS22,
  author    = {Nikita Martynov and
               Irina Krotova and
               Varvara Logacheva and
               Alexander Panchenko and
               Olga Kozlova and
               Nikita Semenov},
  editor    = {Nicoletta Calzolari and
               Fr{\'{e}}d{\'{e}}ric B{\'{e}}chet and
               Philippe Blache and
               Khalid Choukri and
               Christopher Cieri and
               Thierry Declerck and
               Sara Goggi and
               Hitoshi Isahara and
               Bente Maegaard and
               Joseph Mariani and
               H{\'{e}}l{\`{e}}ne Mazo and
               Jan Odijk and
               Stelios Piperidis},
  title     = {RuPAWS: {A} Russian Adversarial Dataset for Paraphrase Identification},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference,
               {LREC} 2022, Marseille, France, 20-25 June 2022},
  pages     = {5683--5691},
  publisher = {European Language Resources Association},
  year      = {2022},
  url       = {https://aclanthology.org/2022.lrec-1.610},
  timestamp = {Mon, 10 Oct 2022 16:57:52 +0200},
  biburl    = {https://dblp.org/rec/conf/lrec/MartynovKLPKS22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{russe2022detoxification,
  title={R{USSE}-2022: Findings of the First {R}ussian Detoxification Task Based on Parallel Corpora},
  author={Dementieva, Daryna and Logacheva, Varvara and Nikishina, Irina and Fenogenova, Alena and Dale, David and Krotova, Irina and Semenov, Nikita and Shavrina, Tatiana and Panchenko, Alexander},
  booktitle={Computational Linguistics and Intellectual Technologies},
  year={2022}
}


@inproceedings{DBLP:conf/eamt/TiedemannT20,
  author    = {J{\"{o}}rg Tiedemann and
               Santhosh Thottingal},
  editor    = {Mikel L. Forcada and
               Andr{\'{e}} Martins and
               Helena Moniz and
               Marco Turchi and
               Arianna Bisazza and
               Joss Moorkens and
               Ana Guerberof Arenas and
               Mary Nurminen and
               Lena Marg and
               Sara Fumega and
               Bruno Martins and
               Fernando Batista and
               Lu{\'{\i}}sa Coheur and
               Carla Parra Escart{\'{\i}}n and
               Isabel Trancoso},
  title     = {{OPUS-MT} - Building open translation services for the World},
  booktitle = {Proceedings of the 22nd Annual Conference of the European Association
               for Machine Translation, {EAMT} 2020, Lisboa, Portugal, November 3-5,
               2020},
  pages     = {479--480},
  publisher = {European Association for Machine Translation},
  year      = {2020},
  url       = {https://aclanthology.org/2020.eamt-1.61/},
  timestamp = {Fri, 06 Aug 2021 00:40:06 +0200},
  biburl    = {https://dblp.org/rec/conf/eamt/TiedemannT20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{santos2018fighting,
  author    = {C{\'{\i}}cero Nogueira dos Santos and
               Igor Melnyk and
               Inkit Padhi},
  editor    = {Iryna Gurevych and
               Yusuke Miyao},
  title     = {Fighting Offensive Language on Social Media with Unsupervised Text
               Style Transfer},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational
               Linguistics, {ACL} 2018, Melbourne, Australia, July 15-20, 2018, Volume
               2: Short Papers},
  pages     = {189--194},
  publisher = {Association for Computational Linguistics},
  year      = {2018},
  url       = {https://aclanthology.org/P18-2031/},
  doi       = {10.18653/v1/P18-2031},
  timestamp = {Fri, 06 Aug 2021 00:41:04 +0200},
  biburl    = {https://dblp.org/rec/conf/acl/SantosMP18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{tran2020towards,
    title = "Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction",
    author = "Tran, Minh  and
      Zhang, Yipeng  and
      Soleymani, Mohammad",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = "December",
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    doi = "10.18653/v1/2020.coling-main.190",
    pages = "2107--2114",
    abstract = "Offensive and abusive language is a pressing problem on social media platforms. In this work, we propose a method for transforming offensive comments, statements containing profanity or offensive language, into non-offensive ones. We design a Retrieve, Generate and Edit unsupervised style transfer pipeline to redact the offensive comments in a word-restricted manner while maintaining a high level of fluency and preserving the content of the original text. We extensively evaluate our method{'}s performance and compare it to previous style transfer models using both automatic metrics and human evaluations. Experimental results show that our method outperforms other models on human evaluations and is the only approach that consistently performs well on all automatic evaluation metrics.",
}

@inproceedings{dale-etal-2021-text,
    title = "Text Detoxification using Large Pre-trained Neural Models",
    author = "Dale, David  and
      Voronov, Anton  and
      Dementieva, Daryna  and
      Logacheva, Varvara  and
      Kozlova, Olga  and
      Semenov, Nikita  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.629",
    doi = "10.18653/v1/2021.emnlp-main.629",
    pages = "7979--7996",
    abstract = "We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-conditional language models and (2) use of paraphrasing models to perform style transfer. We use a well-performing paraphraser guided by style-trained language models to keep the text content and remove toxicity. Our second method uses BERT to replace toxic words with their non-offensive synonyms. We make the method more flexible by enabling BERT to replace mask tokens with a variable number of words. Finally, we present the first large-scale comparative study of style transfer models on the task of toxicity removal. We compare our models with a number of methods for style transfer. The models are evaluated in a reference-free way using a combination of unsupervised style transfer metrics. Both methods we suggest yield new SOTA results.",
}


@misc{jigsaw_multi,
  author = {Jigsaw},
  year = {2020},
  title = {Jigsaw Multilingual Toxic Comment Classification},
  howpublished = {\href{https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification}{https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification}},
  note = {Accessed: 2021-03-01}
}

@inproceedings{DBLP:conf/acl-socialnlp/MoonCL20,
  author    = {Jihyung Moon and
               Won{-}Ik Cho and
               Junbum Lee},
  editor    = {Lun{-}Wei Ku and
               Cheng{-}Te Li},
  title     = {BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection},
  booktitle = {Proceedings of the Eighth International Workshop on Natural Language
               Processing for Social Media, SocialNLP@ACL 2020, Online, July 10,
               2020},
  pages     = {25--31},
  publisher = {Association for Computational Linguistics},
  year      = {2020},
  url       = {https://doi.org/10.18653/v1/2020.socialnlp-1.4},
  doi       = {10.18653/v1/2020.socialnlp-1.4},
  timestamp = {Mon, 09 Aug 2021 16:25:38 +0200},
  biburl    = {https://dblp.org/rec/conf/acl-socialnlp/MoonCL20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/information/VanetikM22,
  author    = {Natalia Vanetik and
               Elisheva Mimoun},
  title     = {Detection of Racist Language in French Tweets},
  journal   = {Inf.},
  volume    = {13},
  number    = {7},
  pages     = {318},
  year      = {2022},
  url       = {https://doi.org/10.3390/info13070318},
  doi       = {10.3390/info13070318},
  timestamp = {Mon, 26 Sep 2022 17:09:15 +0200},
  biburl    = {https://dblp.org/rec/journals/information/VanetikM22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{tox_ru_comments,
  author = {Aleksandr Semiletov},
  year = {2020},
  title = {Toxic Russian Comments},
  howpublished = {\href{https://www.kaggle.com/alexandersemiletov/toxic-russian-comments}{https://www.kaggle.com/alexandersemiletov/toxic-russian-comments}},
  note = {Accessed: 2021-07-22}
}

@inproceedings{lewis-etal-2020-bart,
    title = "{BART}: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
    author = "Lewis, Mike  and
      Liu, Yinhan  and
      Goyal, Naman  and
      Ghazvininejad, Marjan  and
      Mohamed, Abdelrahman  and
      Levy, Omer  and
      Stoyanov, Veselin  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.703",
    doi = "10.18653/v1/2020.acl-main.703",
    pages = "7871--7880",
    abstract = "We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance.",
}

@article{DBLP:journals/jmlr/RaffelSRLNMZLL20,
  author    = {Colin Raffel and
               Noam Shazeer and
               Adam Roberts and
               Katherine Lee and
               Sharan Narang and
               Michael Matena and
               Yanqi Zhou and
               Wei Li and
               Peter J. Liu},
  title     = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text
               Transformer},
  journal   = {J. Mach. Learn. Res.},
  volume    = {21},
  pages     = {140:1--140:67},
  year      = {2020},
  url       = {http://jmlr.org/papers/v21/20-074.html},
  timestamp = {Fri, 05 Feb 2021 15:43:41 +0100},
  biburl    = {https://dblp.org/rec/journals/jmlr/RaffelSRLNMZLL20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{costa2022no,
  title={No Language Left Behind: Scaling Human-Centered Machine Translation},
  author={Costa-juss{\`a}, Marta R and Cross, James and {\c{C}}elebi, Onur and Elbayad, Maha and Heafield, Kenneth and Heffernan, Kevin and Kalbassi, Elahe and Lam, Janice and Licht, Daniel and Maillard, Jean and others},
  journal={arXiv e-prints},
  pages={arXiv--2207},
  year={2022}
}

@inproceedings{xue2021mt5,
  author    = {Linting Xue and
               Noah Constant and
               Adam Roberts and
               Mihir Kale and
               Rami Al{-}Rfou and
               Aditya Siddhant and
               Aditya Barua and
               Colin Raffel},
  editor    = {Kristina Toutanova and
               Anna Rumshisky and
               Luke Zettlemoyer and
               Dilek Hakkani{-}T{\"{u}}r and
               Iz Beltagy and
               Steven Bethard and
               Ryan Cotterell and
               Tanmoy Chakraborty and
               Yichao Zhou},
  title     = {mT5: {A} Massively Multilingual Pre-trained Text-to-Text Transformer},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of
               the Association for Computational Linguistics: Human Language Technologies,
               {NAACL-HLT} 2021, Online, June 6-11, 2021},
  pages     = {483--498},
  publisher = {Association for Computational Linguistics},
  year      = {2021},
  url       = {https://doi.org/10.18653/v1/2021.naacl-main.41},
  doi       = {10.18653/v1/2021.naacl-main.41},
  timestamp = {Fri, 06 Aug 2021 00:41:32 +0200},
  biburl    = {https://dblp.org/rec/conf/naacl/XueCRKASBR21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{tang2020multilingual,
    title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning},
    author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan},
    year={2020},
    eprint={2008.00401},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

@article{el2022multilingual,
  title={A multilingual offensive language detection method based on transfer learning from transformer fine-tuning model},
  author={El-Alami, Fatima-zahra and El Alaoui, Said Ouatik and Nahnahi, Noureddine En},
  journal={Journal of King Saud University-Computer and Information Sciences},
  volume={34},
  number={8},
  pages={6048--6056},
  year={2022},
  publisher={Elsevier}
}

@article{DBLP:journals/csse/WadudMSNS23,
  author    = {Md. Anwar Hussen Wadud and
               Muhammad F. Mridha and
               Jungpil Shin and
               Kamruddin Nur and
               Aloke Kumar Saha},
  title     = {Deep-BERT: Transfer Learning for Classifying Multilingual Offensive
               Texts on Social Media},
  journal   = {Comput. Syst. Sci. Eng.},
  volume    = {44},
  number    = {2},
  pages     = {1775--1791},
  year      = {2023},
  url       = {https://doi.org/10.32604/csse.2023.027841},
  doi       = {10.32604/csse.2023.027841},
  timestamp = {Sun, 02 Oct 2022 15:33:15 +0200},
  biburl    = {https://dblp.org/rec/journals/csse/WadudMSNS23.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/acl/LaiTN22,
  author    = {Huiyuan Lai and
               Antonio Toral and
               Malvina Nissim},
  editor    = {Smaranda Muresan and
               Preslav Nakov and
               Aline Villavicencio},
  title     = {Multilingual Pre-training with Language and Task Adaptation for Multilingual
               Text Style Transfer},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational
               Linguistics (Volume 2: Short Papers), {ACL} 2022, Dublin, Ireland,
               May 22-27, 2022},
  pages     = {262--271},
  publisher = {Association for Computational Linguistics},
  year      = {2022},
  url       = {https://doi.org/10.18653/v1/2022.acl-short.29},
  doi       = {10.18653/v1/2022.acl-short.29},
  timestamp = {Mon, 01 Aug 2022 16:27:42 +0200},
  biburl    = {https://dblp.org/rec/conf/acl/LaiTN22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/acl/KrishnaNGST22,
  author    = {Kalpesh Krishna and
               Deepak Nathani and
               Xavier Garcia and
               Bidisha Samanta and
               Partha Talukdar},
  editor    = {Smaranda Muresan and
               Preslav Nakov and
               Aline Villavicencio},
  title     = {Few-shot Controllable Style Transfer for Low-Resource Multilingual
               Settings},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational
               Linguistics (Volume 1: Long Papers), {ACL} 2022, Dublin, Ireland,
               May 22-27, 2022},
  pages     = {7439--7468},
  publisher = {Association for Computational Linguistics},
  year      = {2022},
  url       = {https://doi.org/10.18653/v1/2022.acl-long.514},
  doi       = {10.18653/v1/2022.acl-long.514},
  timestamp = {Mon, 01 Aug 2022 16:27:47 +0200},
  biburl    = {https://dblp.org/rec/conf/acl/KrishnaNGST22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/wmt/NgYBOAE19,
  author    = {Nathan Ng and
               Kyra Yee and
               Alexei Baevski and
               Myle Ott and
               Michael Auli and
               Sergey Edunov},
  editor    = {Ondrej Bojar and
               Rajen Chatterjee and
               Christian Federmann and
               Mark Fishel and
               Yvette Graham and
               Barry Haddow and
               Matthias Huck and
               Antonio Jimeno{-}Yepes and
               Philipp Koehn and
               Andr{\'{e}} Martins and
               Christof Monz and
               Matteo Negri and
               Aur{\'{e}}lie N{\'{e}}v{\'{e}}ol and
               Mariana L. Neves and
               Matt Post and
               Marco Turchi and
               Karin Verspoor},
  title     = {Facebook FAIR's {WMT19} News Translation Task Submission},
  booktitle = {Proceedings of the Fourth Conference on Machine Translation, {WMT}
               2019, Florence, Italy, August 1-2, 2019 - Volume 2: Shared Task Papers,
               Day 1},
  pages     = {314--319},
  publisher = {Association for Computational Linguistics},
  year      = {2019},
  url       = {https://doi.org/10.18653/v1/w19-5333},
  doi       = {10.18653/v1/w19-5333},
  timestamp = {Thu, 17 Feb 2022 16:43:16 +0100},
  biburl    = {https://dblp.org/rec/conf/wmt/NgYBOAE19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{babakov-etal-2022-large,
    title = "A large-scale computational study of content preservation measures for text style transfer and paraphrase generation",
    author = "Babakov, Nikolay  and
      Dale, David  and
      Logacheva, Varvara  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-srw.23",
    doi = "10.18653/v1/2022.acl-srw.23",
    pages = "300--321",
    abstract = "Text style transfer and paraphrasing of texts are actively growing areas of NLP, dozens of methods for solving these tasks have been recently introduced. In both tasks, the system is supposed to generate a text which should be semantically similar to the input text. Therefore, these tasks are dependent on methods of measuring textual semantic similarity. However, it is still unclear which measures are the best to automatically evaluate content preservation between original and generated text. According to our observations, many researchers still use BLEU-like measures, while there exist more advanced measures including neural-based that significantly outperform classic approaches. The current problem is the lack of a thorough evaluation of the available measures. We close this gap by conducting a large-scale computational study by comparing 57 measures based on different principles on 19 annotated datasets. We show that measures based on cross-encoder models outperform alternative approaches in almost all cases.We also introduce the Mutual Implication Score (MIS), a measure that uses the idea of paraphrasing as a bidirectional entailment and outperforms all other measures on the paraphrase detection task and performs on par with the best measures in the text style transfer task.",
}

@inproceedings{DBLP:conf/icml/HoulsbyGJMLGAG19,
  author    = {Neil Houlsby and
               Andrei Giurgiu and
               Stanislaw Jastrzebski and
               Bruna Morrone and
               Quentin de Laroussilhe and
               Andrea Gesmundo and
               Mona Attariyan and
               Sylvain Gelly},
  editor    = {Kamalika Chaudhuri and
               Ruslan Salakhutdinov},
  title     = {Parameter-Efficient Transfer Learning for {NLP}},
  booktitle = {Proceedings of the 36th International Conference on Machine Learning,
               {ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
  series    = {Proceedings of Machine Learning Research},
  volume    = {97},
  pages     = {2790--2799},
  publisher = {{PMLR}},
  year      = {2019},
  url       = {http://proceedings.mlr.press/v97/houlsby19a.html},
  timestamp = {Tue, 11 Jun 2019 15:37:38 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/HoulsbyGJMLGAG19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2212-10543,
  author    = {Skyler Hallinan and
               Alisa Liu and
               Yejin Choi and
               Maarten Sap},
  title     = {Detoxifying Text with MaRCo: Controllable Revision with Experts and
               Anti-Experts},
  journal   = {CoRR},
  volume    = {abs/2212.10543},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2212.10543},
  doi       = {10.48550/arXiv.2212.10543},
  eprinttype = {arXiv},
  eprint    = {2212.10543},
  timestamp = {Wed, 04 Jan 2023 16:01:37 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2212-10543.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/jmlr/FanBSMEGBCWCGBL21,
  author    = {Angela Fan and
               Shruti Bhosale and
               Holger Schwenk and
               Zhiyi Ma and
               Ahmed El{-}Kishky and
               Siddharth Goyal and
               Mandeep Baines and
               Onur Celebi and
               Guillaume Wenzek and
               Vishrav Chaudhary and
               Naman Goyal and
               Tom Birch and
               Vitaliy Liptchinsky and
               Sergey Edunov and
               Michael Auli and
               Armand Joulin},
  title     = {Beyond English-Centric Multilingual Machine Translation},
  journal   = {J. Mach. Learn. Res.},
  volume    = {22},
  pages     = {107:1--107:48},
  year      = {2021},
  url       = {http://jmlr.org/papers/v22/20-1307.html},
  timestamp = {Mon, 31 Jan 2022 17:23:36 +0100},
  biburl    = {https://dblp.org/rec/journals/jmlr/FanBSMEGBCWCGBL21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/lrec/Creutz18,
  author    = {Mathias Creutz},
  editor    = {Nicoletta Calzolari and
               Khalid Choukri and
               Christopher Cieri and
               Thierry Declerck and
               Sara Goggi and
               K{\^{o}}iti Hasida and
               Hitoshi Isahara and
               Bente Maegaard and
               Joseph Mariani and
               H{\'{e}}l{\`{e}}ne Mazo and
               Asunci{\'{o}}n Moreno and
               Jan Odijk and
               Stelios Piperidis and
               Takenobu Tokunaga},
  title     = {Open Subtitles Paraphrase Corpus for Six Languages},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources
               and Evaluation, {LREC} 2018, Miyazaki, Japan, May 7-12, 2018},
  publisher = {European Language Resources Association {(ELRA)}},
  year      = {2018},
  url       = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/131.html},
  timestamp = {Mon, 19 Aug 2019 15:22:55 +0200},
  biburl    = {https://dblp.org/rec/conf/lrec/Creutz18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/lrec/LisonT16,
  author    = {Pierre Lison and
               J{\"{o}}rg Tiedemann},
  editor    = {Nicoletta Calzolari and
               Khalid Choukri and
               Thierry Declerck and
               Sara Goggi and
               Marko Grobelnik and
               Bente Maegaard and
               Joseph Mariani and
               H{\'{e}}l{\`{e}}ne Mazo and
               Asunci{\'{o}}n Moreno and
               Jan Odijk and
               Stelios Piperidis},
  title     = {OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and
               {TV} Subtitles},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources
               and Evaluation {LREC} 2016, Portoro{\v{z}}, Slovenia, May 23-28, 2016},
  publisher = {European Language Resources Association {(ELRA)}},
  year      = {2016},
  url       = {http://www.lrec-conf.org/proceedings/lrec2016/summaries/947.html},
  timestamp = {Mon, 19 Aug 2019 15:22:18 +0200},
  biburl    = {https://dblp.org/rec/conf/lrec/LisonT16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/wmt/Tiedemann20,
  author    = {J{\"{o}}rg Tiedemann},
  editor    = {Lo{\"{\i}}c Barrault and
               Ondrej Bojar and
               Fethi Bougares and
               Rajen Chatterjee and
               Marta R. Costa{-}juss{\`{a}} and
               Christian Federmann and
               Mark Fishel and
               Alexander Fraser and
               Yvette Graham and
               Paco Guzman and
               Barry Haddow and
               Matthias Huck and
               Antonio Jimeno{-}Yepes and
               Philipp Koehn and
               Andr{\'{e}} Martins and
               Makoto Morishita and
               Christof Monz and
               Masaaki Nagata and
               Toshiaki Nakazawa and
               Matteo Negri},
  title     = {The Tatoeba Translation Challenge - Realistic Data Sets for Low Resource
               and Multilingual {MT}},
  booktitle = {Proceedings of the Fifth Conference on Machine Translation, WMT@EMNLP
               2020, Online, November 19-20, 2020},
  pages     = {1174--1182},
  publisher = {Association for Computational Linguistics},
  year      = {2020},
  url       = {https://aclanthology.org/2020.wmt-1.139/},
  timestamp = {Fri, 06 Aug 2021 00:40:13 +0200},
  biburl    = {https://dblp.org/rec/conf/wmt/Tiedemann20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/acl/JohnMBV19,
  author    = {Vineet John and
               Lili Mou and
               Hareesh Bahuleyan and
               Olga Vechtomova},
  editor    = {Anna Korhonen and
               David R. Traum and
               Llu{\'{\i}}s M{\`{a}}rquez},
  title     = {Disentangled Representation Learning for Non-Parallel Text Style Transfer},
  booktitle = {Proceedings of the 57th Conference of the Association for Computational
               Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019,
               Volume 1: Long Papers},
  pages     = {424--434},
  publisher = {Association for Computational Linguistics},
  year      = {2019},
  url       = {https://doi.org/10.18653/v1/p19-1041},
  doi       = {10.18653/v1/p19-1041},
  timestamp = {Fri, 06 Aug 2021 00:41:04 +0200},
  biburl    = {https://dblp.org/rec/conf/acl/JohnMBV19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}