  Data artifacts incentivize machine learning models to learn non-transferable
generalizations by taking advantage of shortcuts in the data, and there is
growing evidence that data artifacts play a role for the strong results that
deep learning models achieve in recent natural language processing benchmarks.
In this paper, we focus on task-oriented dialogue and investigate whether
popular datasets such as MultiWOZ contain such data artifacts. We found that by
only keeping frequent phrases in the training examples, state-of-the-art models
perform similarly compared to the variant trained with full data, suggesting
they exploit these spurious correlations to solve the task. Motivated by this,
we propose a contrastive learning based framework to encourage the model to
ignore these cues and focus on learning generalisable patterns. We also
experiment with adversarial filtering to remove "easy" training instances so
that the model would focus on learning from the "harder" instances. We conduct
a number of generalization experiments -- e.g., cross-domain/dataset and
adversarial tests -- to assess the robustness of our approach and found that it
works exceptionally well.

Computation and Language