OneLabeler: A Flexible System for Building Data Labeling Tools

Document Title

OneLabeler: A Flexible System for Building Data Labeling Tools

Document Information

• Subject Area: Data labeling, framework design, human-computer interaction
• Keywords: Data labeling, framework, toolkit, interactive machine learning, visual programming

Research Background and Problem

• Problems or Challenges Identified by the Authors:

  • Building data labeling tools to meet diverse task requirements is often time-consuming, costly, and requires interdisciplinary knowledge (interaction design, algorithmic techniques, and software development skills).
  • Existing labeling tools are typically single-purpose applications with limited scalability and customization support.
  • While data labeling tasks across different application domains may share commonalities, directly reusing existing tools is challenging.

• Why This Problem Is Important:

  • In supervised learning, labeled datasets are crucial for model training, and the efficiency and quality of data labeling directly impact the performance of machine learning systems.
  • Developing reusable labeling tools can reduce redundant development efforts, save costs, and address the needs of diverse data types and labeling tasks.

• Research Motivation and Related Work:

  • The authors aim to develop a framework that supports the construction of diverse labeling tools by summarizing the modules and workflows of existing labeling tools, leading to the development of the OneLabeler system.
  • This tool seeks to simplify the development process through visual programming and achieve scalability and customization through modular design.

Solution

• Proposed Method or Solution:

  • A conceptual framework is proposed, categorizing common modules and states in data labeling tasks, including interactive labeling, data selection, model training, feature extraction, default labeling, quality assurance, and six other module types.
  • The OneLabeler system was developed with core features such as modular design, a visual programming interface, static checks, and tool preview functionality.

• Innovative Aspects of the Solution:

  • Introduces a modular composition design for data labeling tools, enabling rapid construction of tools through module assembly and configuration.
  • Proposes a static checking mechanism to guide developers in building correct labeling tools by analyzing workflow structures.
  • Supports multiple types of labeling tasks (e.g., image classification, text labeling) and provides a wide range of built-in components.

• Key Technologies Used in Implementation Steps:

  • Modular Design: Defines data labeling tools as a workflow graph with nodes and edges, where each node corresponds to a module.
  • Visual Programming: Allows developers to edit workflow modules through a graphical interface, requiring little to no text-based coding.
  • Static Checks: Automatically verifies the configuration and dependencies of modules within the workflow to ensure executability.
  • Built-in Modules and Templates: Offers a collection of built-in modules and workflow templates for common data types and labeling tasks.

Research Outcomes

• Specific Outcomes:

  • The authors demonstrated ten example labeling tools built using OneLabeler, including basic tools (e.g., image classification, text labeling) and advanced applications (e.g., hybrid labeling, interactive machine learning systems).
  • User studies showed that developers could efficiently learn and construct labeling tools using OneLabeler.

• Advantages Over Existing Solutions:

  • OneLabeler’s modular design enables users to quickly build labeling tools through visual programming, lowering the development barrier.
  • Its customization features allow developers to extend specific modules or data types, enhancing tool adaptability.
  • Compared to existing single-purpose labeling tools, OneLabeler is more advantageous in supporting multi-scenario, multi-task labeling.

• Experimental or Evaluation Results:

  • In user studies, four tasks were set to evaluate users' ability and time required to complete labeling tool development. Most users completed tasks in a short time, demonstrating its usability and favorable learning curve.
  • The static checking feature helped users efficiently locate and resolve errors.

• Limitations and Future Directions:

  • Limitations:
    • Currently, group labeling workflows are not considered, limiting support for collaborative labeling tools.
    • The provided modules cover most common tasks but may not address certain specialized needs.
  • Future Directions:
    • Expand module types, particularly for quality assurance, stopping analysis, and label design modules.
    • Explore support for group labeling workflows, such as incorporating task allocation and result verification features.
    • Develop a module marketplace to enable developers to share custom modules, enhancing reusability and scalability.
    • Validate its potential applications in more complex interactive machine learning tasks.