  Retrieval-based conversational systems learn to rank response candidates for
a given dialogue context by computing the similarity between their vector
representations. However, training on a single textual form of the multi-turn
context limits the ability of a model to learn representations that generalize
to natural perturbations seen during inference. In this paper we propose a
framework that incorporates augmented versions of a dialogue context into the
learning objective. We utilize contrastive learning as an auxiliary objective
to learn robust dialogue context representations that are invariant to
perturbations injected through the augmentation method. We experiment with four
benchmark dialogue datasets and demonstrate that our framework combines well
with existing augmentation methods and can significantly improve over baseline
BERT-based ranking architectures. Furthermore, we propose a novel data
augmentation method, ConMix, that adds token level perturbations through
stochastic mixing of tokens from other contexts in the batch. We show that our
proposed augmentation method outperforms previous data augmentation approaches,
and provides dialogue representations that are more robust to common
perturbations seen during inference.
