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        <title>A Copy-Augmented Generative Model for Open-Domain Question Answering</title>
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    <abstract>Open-domain question answering is a challenging task with a wide variety of practical applications. Existing modern approaches mostly follow a standard two-stage paradigm: retriever then reader. In this article, we focus on improving the effectiveness of the reader module and propose a novel copy-augmented generative approach that integrates the merits of both extractive and generative readers. In particular, our model is built upon the powerful generative model FiD (CITATION). We enhance the original generative reader by incorporating a pointer network to encourage the model to directly copy words from the retrieved passages. We conduct experiments on the two benchmark datasets, Natural Questions and TriviaQA, and the empirical results demonstrate the performance gains of our proposed approach.</abstract>
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%0 Conference Proceedings
%T A Copy-Augmented Generative Model for Open-Domain Question Answering
%A Liu, Shuang
%A Wang, Dong
%A Li, Xiaoguang
%A Huang, Minghui
%A Ding, Meizhen
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F liu-etal-2022-copy
%X Open-domain question answering is a challenging task with a wide variety of practical applications. Existing modern approaches mostly follow a standard two-stage paradigm: retriever then reader. In this article, we focus on improving the effectiveness of the reader module and propose a novel copy-augmented generative approach that integrates the merits of both extractive and generative readers. In particular, our model is built upon the powerful generative model FiD (CITATION). We enhance the original generative reader by incorporating a pointer network to encourage the model to directly copy words from the retrieved passages. We conduct experiments on the two benchmark datasets, Natural Questions and TriviaQA, and the empirical results demonstrate the performance gains of our proposed approach.
%R 10.18653/v1/2022.acl-short.47
%U https://aclanthology.org/2022.acl-short.47
%U https://doi.org/10.18653/v1/2022.acl-short.47
%P 435-441

Markdown (Informal)

[A Copy-Augmented Generative Model for Open-Domain Question Answering](https://aclanthology.org/2022.acl-short.47) (Liu et al., ACL 2022)

• A Copy-Augmented Generative Model for Open-Domain Question Answering (Liu et al., ACL 2022)

ACL

• Shuang Liu, Dong Wang, Xiaoguang Li, Minghui Huang, and Meizhen Ding. 2022. A Copy-Augmented Generative Model for Open-Domain Question Answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 435–441, Dublin, Ireland. Association for Computational Linguistics.