  Retrieval augmentation, which enhances downstream models by a knowledge
retriever and an external corpus instead of by merely increasing the number of
model parameters, has been successfully applied to many natural language
processing (NLP) tasks such as text classification, question answering and so
on. However, existing methods that separately or asynchronously train the
retriever and downstream model mainly due to the non-differentiability between
the two parts, usually lead to degraded performance compared to end-to-end
joint training. In this paper, we propose Differentiable Retrieval Augmentation
via Generative lANguage modeling(Dragan), to address this problem by a novel
differentiable reformulation. We demonstrate the effectiveness of our proposed
method on a challenging NLP task in e-commerce search, namely query intent
classification. Both the experimental results and ablation study show that the
proposed method significantly and reasonably improves the state-of-the-art
baselines on both offline evaluation and online A/B test.
