Pruning the Index Contents for Memory Efficient Open-Domain QA

• URL: http://arxiv.org/abs/2102.10697v1
• Date: Sun, 21 Feb 2021 21:56:38 GMT
• Title: Pruning the Index Contents for Memory Efficient Open-Domain QA
• Abstract summary: This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches. It proposes the novel R2-D2 pipeline composed of retriever, reranker, extractive reader, generative reader and a simple way to combine them. This work presents a simple approach for pruning the contents of a massive index such that the open-domain QA system altogether with index, OS, and library components fits into 6GiB docker image.
• Score: 0.1529342790344802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches, surpassing the 50% exact match on NaturalQuestions and EfficentQA datasets. Specifically, it proposes the novel R2-D2 (Rank twice, reaD twice) pipeline composed of retriever, reranker, extractive reader, generative reader and a simple way to combine them. Furthermore, previous work often comes with a massive index of external documents that scales in the order of tens of GiB. This work presents a simple approach for pruning the contents of a massive index such that the open-domain QA system altogether with index, OS, and library components fits into 6GiB docker image while retaining only 8% of original index contents and losing only 3% EM accuracy.

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