mteb/InappropriatenessClassification

Tasks

• text-classification

Citation text from the card

@inproceedings{babakov-etal-2021-detecting,
  abstract = {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 inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.},
  address = {Kiyv, Ukraine},
  author = {Babakov, Nikolay  and
Logacheva, Varvara  and
Kozlova, Olga  and
Semenov, Nikita  and
Panchenko, Alexander},
  booktitle = {Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing},
  editor = {Babych, Bogdan  and
Kanishcheva, Olga  and
Nakov, Preslav  and
Piskorski, Jakub  and
Pivovarova, Lidia  and
Starko, Vasyl  and
Steinberger, Josef  and
Yangarber, Roman  and
Marci{\'n}czuk, Micha{\l}  and
Pollak, Senja  and
P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
Rob

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