About This Document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Ikuya Yamada
- sl:arxiv_num : 2106.00882
- sl:arxiv_published : 2021-06-02T01:34:42Z
- sl:arxiv_summary : Most state-of-the-art open-domain question answering systems use a neural
retrieval model to encode passages into continuous vectors and extract them
from a knowledge source. However, such retrieval models often require large
memory to run because of the massive size of their passage index. In this
paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural
retrieval model that integrates a learning-to-hash technique into the
state-of-the-art Dense Passage Retriever (DPR) to represent the passage index
using compact binary codes rather than continuous vectors. BPR is trained with
a multi-task objective over two tasks: efficient candidate generation based on
binary codes and accurate reranking based on continuous vectors. Compared with
DPR, BPR substantially reduces the memory cost from 65GB to 2GB without a loss
of accuracy on two standard open-domain question answering benchmarks: Natural
Questions and TriviaQA. Our code and trained models are available at
https://github.com/studio-ousia/bpr.@en
- sl:arxiv_title : Efficient Passage Retrieval with Hashing for Open-domain Question Answering@en
- sl:arxiv_updated : 2021-06-02T01:34:42Z
- sl:bookmarkOf : https://arxiv.org/abs/2106.00882
- sl:creationDate : 2021-06-03
- sl:creationTime : 2021-06-03T11:11:35Z
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