  Dense retrieval is becoming one of the standard approaches for document and
passage ranking. The dual-encoder architecture is widely adopted for scoring
question-passage pairs due to its efficiency and high performance. Typically,
dense retrieval models are evaluated on clean and curated datasets. However,
when deployed in real-life applications, these models encounter noisy
user-generated text. That said, the performance of state-of-the-art dense
retrievers can substantially deteriorate when exposed to noisy text. In this
work, we study the robustness of dense retrievers against typos in the user
question. We observe a significant drop in the performance of the dual-encoder
model when encountering typos and explore ways to improve its robustness by
combining data augmentation with contrastive learning. Our experiments on two
large-scale passage ranking and open-domain question answering datasets show
that our proposed approach outperforms competing approaches. Additionally, we
perform a thorough analysis on robustness. Finally, we provide insights on how
different typos affect the robustness of embeddings differently and how our
method alleviates the effect of some typos but not of others.
