Two-Step Question Retrieval for Open-Domain QA
Yeon Seonwoo, Juhee Son, Jiho Jin, Sang‐Woo Lee, Ji‐Hoon Kim, Jung-Woo Ha, Alice H. Oh · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
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 secondstep 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. 1 * These authors contributed equally. 1 The implementation of SQuID has been released at https://github.com/yeonsw/SQuID.git