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        <title>Step or Not: Discriminator for The Real Instructions in User-generated Recipes</title>
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        <namePart type="given">Shintaro</namePart>
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        <namePart type="given">Takahiko</namePart>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Wei</namePart>
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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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            <namePart type="given">Afshin</namePart>
            <namePart type="family">Rahimi</namePart>
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    <abstract>In a recipe sharing service, users publish recipe instructions in the form of a series of steps. However, some of the “steps” are not actually part of the cooking process. Specifically, advertisements of recipes themselves (e.g., “introduced on TV”) and comments (e.g., “Thanks for many messages”) may often be included in the step section of the recipe, like the recipe author’s communication tool. However, such fake steps can cause problems when using recipe search indexing or when being spoken by devices such as smart speakers. As presented in this talk, we have constructed a discriminator that distinguishes between such a fake step and the step actually used for cooking. This project includes, but is not limited to, the creation of annotation data by classifying and analyzing recipe steps and the construction of identification models. Our models use only text information to identify the step. In our test, machine learning models achieved higher accuracy than rule-based methods that use manually chosen clue words.</abstract>
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%0 Conference Proceedings
%T Step or Not: Discriminator for The Real Instructions in User-generated Recipes
%A Inuzuka, Shintaro
%A Ito, Takahiko
%A Harashima, Jun
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F inuzuka-etal-2018-step
%X In a recipe sharing service, users publish recipe instructions in the form of a series of steps. However, some of the “steps” are not actually part of the cooking process. Specifically, advertisements of recipes themselves (e.g., “introduced on TV”) and comments (e.g., “Thanks for many messages”) may often be included in the step section of the recipe, like the recipe author’s communication tool. However, such fake steps can cause problems when using recipe search indexing or when being spoken by devices such as smart speakers. As presented in this talk, we have constructed a discriminator that distinguishes between such a fake step and the step actually used for cooking. This project includes, but is not limited to, the creation of annotation data by classifying and analyzing recipe steps and the construction of identification models. Our models use only text information to identify the step. In our test, machine learning models achieved higher accuracy than rule-based methods that use manually chosen clue words.
%R 10.18653/v1/W18-6128
%U https://aclanthology.org/W18-6128
%U https://doi.org/10.18653/v1/W18-6128
%P 214

Markdown (Informal)

[Step or Not: Discriminator for The Real Instructions in User-generated Recipes](https://aclanthology.org/W18-6128) (Inuzuka et al., WNUT 2018)

• Step or Not: Discriminator for The Real Instructions in User-generated Recipes (Inuzuka et al., WNUT 2018)

ACL

• Shintaro Inuzuka, Takahiko Ito, and Jun Harashima. 2018. Step or Not: Discriminator for The Real Instructions in User-generated Recipes. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, page 214, Brussels, Belgium. Association for Computational Linguistics.