  Integrating logical reasoning and machine learning by approximating logical
inference with differentiable operators is a widely used technique in
Neuro-Symbolic systems.
  However, some differentiable operators could bring a significant bias during
backpropagation and degrade the performance of Neuro-Symbolic learning.
  In this paper, we reveal that this bias, named \textit{Implication Bias} is
common in loss functions derived from fuzzy logic operators.
  Furthermore, we propose a simple yet effective method to transform the biased
loss functions into \textit{Reduced Implication-bias Logic Loss (RILL)} to
address the above problem.
  Empirical study shows that RILL can achieve significant improvements compared
with the biased logic loss functions, especially when the knowledge base is
incomplete, and keeps more robust than the compared methods when labelled data
is insufficient.
