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- W3174863418 abstract "Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results. While generally effective, these models fail to provide a satisfying answer when all retrieved candidates are of poor quality, even if they contain correct information. In AS2, models are trained to select the best answer sentence among a set of candidates retrieved for a given question. In this work, we propose to generate answers from a set of AS2 top candidates. Rather than selecting the best candidate, we train a sequence to sequence transformer model to generate an answer from a candidate set. Our tests on three English AS2 datasets show improvement up to 32 absolute points in accuracy over the state of the art." @default.
- W3174863418 created "2021-07-05" @default.
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- W3174863418 date "2021-01-01" @default.
- W3174863418 modified "2023-10-16" @default.
- W3174863418 title "Answer Generation for Retrieval-based Question Answering Systems" @default.
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- W3174863418 doi "https://doi.org/10.18653/v1/2021.findings-acl.374" @default.
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