  Researchers recently found out that sometimes language models achieve high
accuracy on benchmark data set, but they can not generalize very well with even
little changes to the original data set. This is sometimes due to data
artifacts, model is learning the spurious correlation between tokens and
labels, instead of the semantics and logic. In this work, we analyzed SNLI data
and visualized such spurious correlations. We proposed an adaptive up-sampling
algorithm to correct the data artifacts, which is simple and effective, and
does not need human edits or annotation. We did an experiment applying the
algorithm to fix the data artifacts in SNLI data and the model trained with
corrected data performed significantly better than the model trained with raw
SNLI data, overall, as well as on the subset we corrected.
