Our Papers

Rationale-Guided Few-Shot Classification to Detect Abusive Language

Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, models trained using these datasets do not perform well in cross-domain …

On the rise of fear speech in online social media

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle …

HateMM: A Multi-Modal Dataset for Hate Video Classification

Hate speech has become one of the most significant issues inmodern society, having implications in both the online and theoffline world. Due to this, hate speech research has recentlygained a lot of traction. However, most of the work has pri-marily …

Multilingual Abusive Comment Detection at Scale for Indic Languages

Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging challenge for automated detection where code-mixing or more than one …

CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech

Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online hate. However, since these suggestions are from a vanilla generation …

HateCheckHIn: Evaluating Hindi Hate Speech Detection Models

Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging challenge for automated detection where code-mixing or more than one …