  Entity typing aims at predicting one or more words that describe the type(s)
of a specific mention in a sentence. Due to shortcuts from surface patterns to
annotated entity labels and biased training, existing entity typing models are
subject to the problem of spurious correlations. To comprehensively investigate
the faithfulness and reliability of entity typing methods, we first
systematically define distinct kinds of model biases that are reflected mainly
from spurious correlations. Particularly, we identify six types of existing
model biases, including mention-context bias, lexical overlapping bias, named
entity bias, pronoun bias, dependency bias, and overgeneralization bias. To
mitigate model biases, we then introduce a counterfactual data augmentation
method. By augmenting the original training set with their debiased
counterparts, models are forced to fully comprehend sentences and discover the
fundamental cues for entity typing, rather than relying on spurious
correlations for shortcuts. Experimental results on the UFET dataset show our
counterfactual data augmentation approach helps improve generalization of
different entity typing models with consistently better performance on both the
original and debiased test sets.
