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        <title>Simple and Effective Retrieve-Edit-Rerank Text Generation</title>
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        <namePart type="given">Nabil</namePart>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and learn a model to edit it to produce the final output. We propose to extend this framework with a simple and effective post-generation ranking approach. Our framework (i) retrieves several potentially relevant outputs for each input, (ii) edits each candidate independently, and (iii) re-ranks the edited candidates to select the final output. We use a standard editing model with simple task-specific re-ranking approaches, and we show empirically that this approach outperforms existing, significantly more complex methodologies. Experiments on two machine translation (MT) datasets show new state-of-art results. We also achieve near state-of-art performance on the Gigaword summarization dataset, where our analyses show that there is significant room for performance improvement with better candidate output selection in future work.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.228</identifier>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T Simple and Effective Retrieve-Edit-Rerank Text Generation
%A Hossain, Nabil
%A Ghazvininejad, Marjan
%A Zettlemoyer, Luke
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F hossain-etal-2020-simple
%X Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and learn a model to edit it to produce the final output. We propose to extend this framework with a simple and effective post-generation ranking approach. Our framework (i) retrieves several potentially relevant outputs for each input, (ii) edits each candidate independently, and (iii) re-ranks the edited candidates to select the final output. We use a standard editing model with simple task-specific re-ranking approaches, and we show empirically that this approach outperforms existing, significantly more complex methodologies. Experiments on two machine translation (MT) datasets show new state-of-art results. We also achieve near state-of-art performance on the Gigaword summarization dataset, where our analyses show that there is significant room for performance improvement with better candidate output selection in future work.
%R 10.18653/v1/2020.acl-main.228
%U https://aclanthology.org/2020.acl-main.228
%U https://doi.org/10.18653/v1/2020.acl-main.228
%P 2532-2538

Markdown (Informal)

[Simple and Effective Retrieve-Edit-Rerank Text Generation](https://aclanthology.org/2020.acl-main.228) (Hossain et al., ACL 2020)

• Simple and Effective Retrieve-Edit-Rerank Text Generation (Hossain et al., ACL 2020)

ACL

• Nabil Hossain, Marjan Ghazvininejad, and Luke Zettlemoyer. 2020. Simple and Effective Retrieve-Edit-Rerank Text Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2532–2538, Online. Association for Computational Linguistics.