Explaining Toxic Text via Knowledge Enhanced Text Generation

Anonymous

Paper Link: https://openreview.net/forum?id=MW7BLqZEyAp

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract:

Warning: This paper contains content that is offensive and may be upsetting.

Biased or toxic speech can be harmful to various demographic groups. Therefore, it is not only important for models to detect these speech, but to also output explanations of why a given text is toxic. Previous literature has mostly focused on classifying and detecting toxic speech, and existing efforts on explaining stereotypes in toxic speech mainly use standard text generation approaches, resulting in generic and repetitive explanations. Building on these prior works, we introduce a novel knowledge-informed encoder-decoder framework to utilize multiple knowledge sources to generate implications of biased text.

Experiments show that our knowledge informed models outperform prior state-of-the-art models significantly, and can generate detailed explanations of stereotypes in toxic speech compared to baselines, both quantitatively and qualitatively.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Rohit Sridhar

Copyright Consent Job Title: Rohit Sridhar

Copyright Consent Name And Address: Rohit Sridhar