Semanlink - [1906.00300] Latent Retrieval for Weakly Supervised Open Domain Question Answering

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Au sujet de ce document

sl:arxiv_author :
sl:arxiv_firstAuthor : Kenton Lee
sl:arxiv_num : 1906.00300
sl:arxiv_published : 2019-06-01T22:02:39Z
sl:arxiv_summary : Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.@en
sl:arxiv_title : Latent Retrieval for Weakly Supervised Open Domain Question Answering@en
sl:arxiv_updated : 2019-06-27T21:06:12Z
sl:bookmarkOf : https://arxiv.org/abs/1906.00300
sl:creationDate : 2022-01-11
sl:creationTime : 2022-01-11T11:06:38Z

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Bookmark of: https://arxiv.org/abs/1906.00300

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> **approach for training dense retrieval models that does not require any labeled training data**. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples.
>
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>
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>
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[Tweet](doc:2022/07/devendra_singh_sachan_sur_twitt)

> Given an
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structs
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>
> The
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