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        <title>Bridging Multi-disciplinary Collaboration Challenges in ML Development via Domain Knowledge Elicitation</title>
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            <title>Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances</title>
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        <name type="personal">
            <namePart type="given">Eduard</namePart>
            <namePart type="family">Dragut</namePart>
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        <name type="personal">
            <namePart type="given">Yunyao</namePart>
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    <abstract>Building a machine learning model in a sophisticated domain is a time-consuming process, partially due to the steep learning curve of domain knowledge for data scientists. We introduce Ziva, an interface for supporting domain knowledge from domain experts to data scientists in two ways: (1) a concept creation interface where domain experts extract important concept of the domain and (2) five kinds of justification elicitation interfaces that solicit elicitation how the domain concept are expressed in data instances.</abstract>
    <identifier type="citekey">park-2021-bridging</identifier>
    <identifier type="doi">10.18653/v1/2021.dash-1.7</identifier>
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%0 Conference Proceedings
%T Bridging Multi-disciplinary Collaboration Challenges in ML Development via Domain Knowledge Elicitation
%A Park, Soya
%Y Dragut, Eduard
%Y Li, Yunyao
%Y Popa, Lucian
%Y Vucetic, Slobodan
%S Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F park-2021-bridging
%X Building a machine learning model in a sophisticated domain is a time-consuming process, partially due to the steep learning curve of domain knowledge for data scientists. We introduce Ziva, an interface for supporting domain knowledge from domain experts to data scientists in two ways: (1) a concept creation interface where domain experts extract important concept of the domain and (2) five kinds of justification elicitation interfaces that solicit elicitation how the domain concept are expressed in data instances.
%R 10.18653/v1/2021.dash-1.7
%U https://aclanthology.org/2021.dash-1.7
%U https://doi.org/10.18653/v1/2021.dash-1.7
%P 44-46

Markdown (Informal)

[Bridging Multi-disciplinary Collaboration Challenges in ML Development via Domain Knowledge Elicitation](https://aclanthology.org/2021.dash-1.7) (Park, DaSH 2021)

• Bridging Multi-disciplinary Collaboration Challenges in ML Development via Domain Knowledge Elicitation (Park, DaSH 2021)

ACL

• Soya Park. 2021. Bridging Multi-disciplinary Collaboration Challenges in ML Development via Domain Knowledge Elicitation. In Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances, pages 44–46, Online. Association for Computational Linguistics.