Bridging Multi-disciplinary Collaboration Challenges in ML Development via Domain Knowledge Elicitation

Soya Park


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.
Anthology ID:
2021.dash-1.7
Volume:
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances
Month:
June
Year:
2021
Address:
Online
Venues:
DaSH | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
44–46
Language:
URL:
https://aclanthology.org/2021.dash-1.7
DOI:
10.18653/v1/2021.dash-1.7
Bibkey:
Cite (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.
Cite (Informal):
Bridging Multi-disciplinary Collaboration Challenges in ML Development via Domain Knowledge Elicitation (Park, DaSH 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.dash-1.7.pdf