Muted: Multilingual Targeted Offensive Speech Identification and
  Visualization
Christoph Tillmann Aashka Trivedi Sara Rosenthal Santosh Borse Rong Zhang Avirup Sil Bishwaranjan Bhattacharjee
Offensive language such as hate, abuse, and profanity (HAP) occurs in various
content on the web. While previous work has mostly dealt with sentence level
annotations, there have been a few recent attempts to identify offensive spans
as well. We build upon this work and introduce Muted, a system to identify
multilingual HAP content by displaying offensive arguments and their targets
using heat maps to indicate their intensity. Muted can leverage any
transformer-based HAP-classification model and its attention mechanism
out-of-the-box to identify toxic spans, without further fine-tuning. In
addition, we use the spaCy library to identify the specific targets and
arguments for the words predicted by the attention heatmaps. We present the
model's performance on identifying offensive spans and their targets in
existing datasets and present new annotations on German text. Finally, we
demonstrate our proposed visualization tool on multilingual inputs.