Guideline Bias in Wizard-of-Oz Dialogues
Victor Petrén Bach Hansen, Anders Søgaard · 2021
NLP models struggle with generalization due to sampling and annotator bias.This paper focuses on a different kind of bias that has received very little attention: guideline bias, i.e., the bias introduced by how our annotator guidelines are formulated.We examine two recently introduced dialogue datasets, CCPE-M and Taskmaster-1, both collected by trained assistants in a Wizard-of-Oz set-up.For CCPE-M, we show how a simple lexical bias for the word like in the guidelines biases the data collection.This bias, in effect, leads to poor performance on data without this bias: a preference elicitation architecture based on BERT suffers a 5.3% absolute drop in performance, when like is replaced with a synonymous phrase, and a 13.2% drop in performance when evaluated on out-of-sample data.For Taskmaster-1, we show how the order in which instructions are presented, biases the data collection.