Empirical Analysis of Multi-Task Learning for Reducing Model Bias in
  Toxic Comment Detection
  With the recent rise of toxicity in online conversations on social media
platforms, using modern machine learning algorithms for toxic comment detection
has become a central focus of many online applications. Researchers and
companies have developed a variety of models to identify toxicity in online
conversations, reviews, or comments with mixed successes. However, many
existing approaches have learned to incorrectly associate non-toxic comments
that have certain trigger-words (e.g. gay, lesbian, black, muslim) as a
potential source of toxicity. In this paper, we evaluate several
state-of-the-art models with the specific focus of reducing model bias towards
these commonly-attacked identity groups. We propose a multi-task learning model
with an attention layer that jointly learns to predict the toxicity of a
comment as well as the identities present in the comments in order to reduce
this bias. We then compare our model to an array of shallow and deep-learning
models using metrics designed especially to test for unintended model bias
within these identity groups.
