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• W2974002024 abstract "A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain QA typically relies on off-the-shelf information retrieval (IR) techniques to retrieve textbf{answer passages}, i.e., the passages containing the groundtruth answers. However, IR-based approaches are insufficient for multi-hop questions, as the topic of the second or further hops is not explicitly covered by the question. To resolve this issue, we introduce a new sub-problem of open-domain multi-hop QA, which aims to recognize the bridge (emph{i.e.}, the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model. This model, the textbf{bridge reasoner}, is trained with a weakly supervised signal and produces the candidate answer passages for the textbf{passage reader} to extract the answer. On the full-wiki HotpotQA benchmark, we significantly improve the baseline method by 14 point F1. Without using any memory-inefficient contextual embeddings, our result is also competitive with the state-of-the-art that applies BERT in multiple modules." @default.
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• W2974002024 date "2019-09-17" @default.
• W2974002024 modified "2023-09-23" @default.
• W2974002024 title "Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering" @default.
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• W2974002024 doi "https://doi.org/10.48550/arxiv.1909.07597" @default.
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