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        <title>Routing Enforced Generative Model for Recipe Generation</title>
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        <namePart type="given">Zhiwei</namePart>
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    <name type="personal">
        <namePart type="given">Xiaojun</namePart>
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            <namePart type="given">Trevor</namePart>
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    <abstract>One of the most challenging part of recipe generation is to deal with the complex restrictions among the input ingredients. Previous researches simplify the problem by treating the inputs independently and generating recipes containing as much information as possible. In this work, we propose a routing method to dive into the content selection under the internal restrictions. The routing enforced generative model (RGM) can generate appropriate recipes according to the given ingredients and user preferences. Our model yields new state-of-the-art results on the recipe generation task with significant improvements on BLEU, F1 and human evaluation.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.311</identifier>
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%0 Conference Proceedings
%T Routing Enforced Generative Model for Recipe Generation
%A Yu, Zhiwei
%A Zang, Hongyu
%A Wan, Xiaojun
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yu-etal-2020-routing
%X One of the most challenging part of recipe generation is to deal with the complex restrictions among the input ingredients. Previous researches simplify the problem by treating the inputs independently and generating recipes containing as much information as possible. In this work, we propose a routing method to dive into the content selection under the internal restrictions. The routing enforced generative model (RGM) can generate appropriate recipes according to the given ingredients and user preferences. Our model yields new state-of-the-art results on the recipe generation task with significant improvements on BLEU, F1 and human evaluation.
%R 10.18653/v1/2020.emnlp-main.311
%U https://aclanthology.org/2020.emnlp-main.311
%U https://doi.org/10.18653/v1/2020.emnlp-main.311
%P 3797-3806

Markdown (Informal)

[Routing Enforced Generative Model for Recipe Generation](https://aclanthology.org/2020.emnlp-main.311) (Yu et al., EMNLP 2020)

• Routing Enforced Generative Model for Recipe Generation (Yu et al., EMNLP 2020)

ACL

• Zhiwei Yu, Hongyu Zang, and Xiaojun Wan. 2020. Routing Enforced Generative Model for Recipe Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3797–3806, Online. Association for Computational Linguistics.