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- W3209173397 abstract "We propose a new model, DocHopper, that iteratively attends to different parts of long, hierarchically structured documents to answer complex questions. Similar to multi-hop question-answering (QA) systems, at each step, DocHopper uses a query $q$ to attend to information from a document, combines this ``retrieved'' information with $q$ to produce the next query. However, in contrast to most previous multi-hop QA systems, DocHopper is able to ``retrieve'' either short passages or long sections of the document, thus emulating a multi-step process of ``navigating'' through a long document to answer a question. To enable this novel behavior, DocHopper does not combine document information with $q$ by concatenating text to the text of $q$, but by combining a compact neural representation of $q$ with a compact neural representation of a hierarchical part of the document, which can potentially be quite large. We experiment with DocHopper on four different QA tasks that require reading long and complex documents to answer multi-hop questions, and show that DocHopper achieves state-of-the-art results on three of the datasets. Additionally, DocHopper is efficient at inference time, being 3--10 times faster than the baselines." @default.
- W3209173397 created "2021-11-08" @default.
- W3209173397 creator A5051617344 @default.
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- W3209173397 date "2021-05-31" @default.
- W3209173397 modified "2023-09-26" @default.
- W3209173397 title "Iterative Hierarchical Attention for Answering Complex Questions over Long Documents" @default.
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- W3209173397 doi "https://doi.org/10.48550/arxiv.2106.00200" @default.
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