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• W3217083402 abstract "Phrase-indexed question answering (PIQA) seeks to improve the inference speed of question answering (QA) models by enforcing complete independence of the document encoder from the question encoder, and it shows that the constrained model can achieve significant efficiency at the cost of its accuracy. In this paper, we aim to build a model under the PIQA constraint while reducing its accuracy gap with the unconstrained QA models. We propose a novel framework—AnsDR, which consists of an answer boundary detector (AnsD) and an answer candidate ranker (AnsR). More specifically, AnsD is a QA model under the PIQA architecture and it is designed to identify the rough answer boundaries; and AnsR is a lightweight ranking model to finely re-rank the potential candidates without losing the efficiency. We perform the extensive experiments on public datasets. The experimental results show that the proposed method achieves the state of the art on the PIQA task." @default.
• W3217083402 created "2021-12-06" @default.
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• W3217083402 date "2022-09-01" @default.
• W3217083402 modified "2023-10-09" @default.
• W3217083402 title "Learning to Combine Answer Boundary Detection and Answer Re‐ranking for Phrase‐Indexed Question Answering" @default.
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• W3217083402 doi "https://doi.org/10.1049/cje.2021.00.079" @default.
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