Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Logacheva, Varvara"'
Publikováno v:
Natural Language Processing and Information Systems. NLDB 2022. Lecture Notes in Computer Science, vol 13286. Springer, Cham, p.437--448
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 dom
Externí odkaz:
http://arxiv.org/abs/2206.09676
Toxicity on the Internet, such as hate speech, offenses towards particular users or groups of people, or the use of obscene words, is an acknowledged problem. However, there also exist other types of inappropriate messages which are usually not viewe
Externí odkaz:
http://arxiv.org/abs/2203.02392
Autor:
Nikishina, Irina, Tikhomirov, Mikhail, Logacheva, Varvara, Nazarov, Yuriy, Panchenko, Alexander, Loukachevitch, Natalia
Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with the hypo-hypernym ("class-subclass") relationship. With the rapid growth of
Externí odkaz:
http://arxiv.org/abs/2201.08598
Autor:
Dale, David, Voronov, Anton, Dementieva, Daryna, Logacheva, Varvara, Kozlova, Olga, Semenov, Nikita, Panchenko, Alexander
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
Externí odkaz:
http://arxiv.org/abs/2109.08914
Autor:
Dementieva, Daryna, Moskovskiy, Daniil, Logacheva, Varvara, Dale, David, Kozlova, Olga, Semenov, Nikita, Panchenko, Alexander
We introduce the first study of automatic detoxification of Russian texts to combat offensive language. Such a kind of textual style transfer can be used, for instance, for processing toxic content in social media. While much work has been done for t
Externí odkaz:
http://arxiv.org/abs/2105.09052
Not all topics are equally "flammable" in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield
Externí odkaz:
http://arxiv.org/abs/2103.05345
Ontologies, taxonomies, and thesauri are used in many NLP tasks. However, most studies are focused on the creation of these lexical resources rather than the maintenance of the existing ones. Thus, we address the problem of taxonomy enrichment. We ex
Externí odkaz:
http://arxiv.org/abs/2011.11536
This paper describes the results of the first shared task on taxonomy enrichment for the Russian language. The participants were asked to extend an existing taxonomy with previously unseen words: for each new word their systems should provide a ranke
Externí odkaz:
http://arxiv.org/abs/2005.11176
Autor:
Logacheva, Varvara, Teslenko, Denis, Shelmanov, Artem, Remus, Steffen, Ustalov, Dmitry, Kutuzov, Andrey, Artemova, Ekaterina, Biemann, Chris, Ponzetto, Simone Paolo, Panchenko, Alexander
Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of s
Externí odkaz:
http://arxiv.org/abs/2003.06651
Autor:
Dinan, Emily, Logacheva, Varvara, Malykh, Valentin, Miller, Alexander, Shuster, Kurt, Urbanek, Jack, Kiela, Douwe, Szlam, Arthur, Serban, Iulian, Lowe, Ryan, Prabhumoye, Shrimai, Black, Alan W, Rudnicky, Alexander, Williams, Jason, Pineau, Joelle, Burtsev, Mikhail, Weston, Jason
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots. Some key takeaways from the competition are: (i) pretrained Transformer variants are currently the best performi
Externí odkaz:
http://arxiv.org/abs/1902.00098