Detecting inappropriate messages
In this talk, we describe the methodology of collecting and labelling a dataset for appropriateness, collect two datasets and release pre-trained classification models.
Overview
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 on 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 to define 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 in Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data. The talk is based on the recent publication at the BSNLP workshop at EACL-2021 conference.
Speaker
Nikolay Babakov
SkoltechResearch EngineerRecording
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