OneLabeler: A Flexible System for Building Data Labeling Tools

被引：16

作者：

Zhang, Yu ^{[1
]}

Wang, Yun ^{[2
]}

Zhang, Haidong ^{[2
]}

Zhu, Bin ^{[2
]}

Chen, Siming ^{[3
]}

Zhang, Dongmei ^{[2
]}

机构：

[1] Univ Oxford, Oxford, England

[2] Microsoft Res Asia, Beijing, Peoples R China

[3] Fudan Univ, Shanghai, Peoples R China

来源：

PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22) | 2022年

关键词：

data labeling; framework; toolkit; interactive machine learning; visual programming; CATEGORIZATION;

D O I：

10.1145/3491102.3517612

中图分类号：

学科分类号：

摘要：

Labeled datasets are essential for supervised machine learning. Various data labeling tools have been built to collect labels in different usage scenarios. However, developing labeling tools is timeconsuming, costly, and expertise-demanding on software development. In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios. The framework consists of common modules and states in labeling tools summarized through coding of existing tools. OneLabeler supports confguration and composition of common software modules through visual programming to build data labeling tools. A module can be a human, machine, or mixed computation procedure in data labeling. We demonstrate the expressiveness and utility of the system through ten example labeling tools built with OneLabeler. A user study with developers provides evidence that OneLabeler supports efcient building of diverse data labeling tools.

引用

页数：22

共 88 条

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