Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models

TL;DR

This paper introduces Ziva, a framework that enables domain experts to efficiently share essential knowledge with data scientists, improving NLP model development while reducing expert burden.

Contribution

The paper presents Ziva, a novel framework that guides domain experts in sharing structured domain knowledge to data scientists for NLP models.

Findings

Domain experts effectively use Ziva to share rich knowledge.

Ziva's output helps data scientists understand domain essentials.

Using Ziva reduces expert stress and enhances knowledge transfer.

Abstract

Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models. While input from domain experts could offer valuable help, such input is often limited, expensive, and generally not in a form readily consumable by a model development pipeline. In this paper, we propose Ziva, a framework to guide domain experts in sharing essential domain knowledge to data scientists for building NLP models. With Ziva, experts are able to distill and share their domain knowledge using domain concept extractors and five types of label justification over a representative data sample. The design of Ziva is informed by preliminary interviews with data scientists, in order to understand current practices of domain knowledge acquisition process for ML development projects. To assess our design, we run a mix-method case-study to evaluate how Ziva can facilitate interaction of domain experts and data scientists. Our results highlight that (1) domain experts are able to use Ziva to provide rich domain knowledge, while maintaining low mental load and stress levels; and (2) data scientists find Ziva's output helpful for learning essential information about the domain, offering scalability of information, and lowering the burden on domain experts to share knowledge. We conclude this work by experimenting with building NLP models using the Ziva output by our case study.