Learning to love diligent trolls: Accounting for rater effects in the
  dialogue safety task
Michael John Ilagan
Chatbots have the risk of generating offensive utterances, which must be
avoided. Post-deployment, one way for a chatbot to continuously improve is to
source utterance/label pairs from feedback by live users. However, among users
are trolls, who provide training examples with incorrect labels. To de-troll
training data, previous work removed training examples that have high
user-aggregated cross-validation (CV) error. However, CV is expensive; and in a
coordinated attack, CV may be overwhelmed by trolls in number and in
consistency among themselves. In the present work, I address both limitations
by proposing a solution inspired by methodology in automated essay scoring
(AES): have multiple users rate each utterance, then perform latent class
analysis (LCA) to infer correct labels. As it does not require GPU
computations, LCA is inexpensive. In experiments, I found that the AES-like
solution can infer training labels with high accuracy when trolls are
consistent, even when trolls are the majority.