  The retriever-reader pipeline has shown promising performance in open-domain
QA but suffers from a very slow inference speed. Recently proposed question
retrieval models tackle this problem by indexing question-answer pairs and
searching for similar questions. These models have shown a significant increase
in inference speed, but at the cost of lower QA performance compared to the
retriever-reader models. This paper proposes a two-step question retrieval
model, SQuID (Sequential Question-Indexed Dense retrieval) and distant
supervision for training. SQuID uses two bi-encoders for question retrieval.
The first-step retriever selects top-k similar questions, and the second-step
retriever finds the most similar question from the top-k questions. We evaluate
the performance and the computational efficiency of SQuID. The results show
that SQuID significantly increases the performance of existing question
retrieval models with a negligible loss on inference speed.
