Two-Step Question Retrieval for Open-Domain QA

• URL: http://arxiv.org/abs/2205.09393v1
• Date: Thu, 19 May 2022 08:46:14 GMT
• Title: Two-Step Question Retrieval for Open-Domain QA
• Authors: Yeon Seonwoo, Juhee Son, Jiho Jin, Sang-Woo Lee, Ji-Hoon Kim, Jung-Woo Ha, Alice Oh
• Abstract summary: 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. SQuID significantly increases the performance of existing question retrieval models with a negligible loss on inference speed.
• Score: 27.37731471419776
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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