11 Interfaces for Data Annotation
- Understanding basic Human-Computer Interaction (HCI) principles.
- Applying HCI principles in annotation interfaces.
- Combining human and machine intelligence to maximize the strengths of each.
- Implementing interfaces with different levels of machine learning integration
- Adding machine learning to applications without disrupting existing work practices.
In the last 10 Chapters, we have covered everything about Human-in-the-Loop Machine Learning, except for the vital component of the human-machine interface! This chapter covers how to build interfaces that maximize the efficiency and accuracy of the annotations. This chapter also covers the trade-offs: there is no one set of interface conventions that can be applied to every task, so you must make an informed decision about what is best for your task and your annotators.
11.1 Basic Principles of Human-Computer Interaction
11.1.1 Introduction to Affordance, Feedback and Agency
11.1.2 Designing Interfaces for Annotation
11.1.3 Minimizing eye movement and scrolling
11.1.4 Keyboard shortcuts and input devices
11.2 Breaking the rules effectively
11.2.1 Scrolling for batch annotation
11.2.2 Foot pedals
11.2.3 Audio inputs
11.3 Priming in Annotation Interfaces
11.3.1 Repetition Priming
11.3.2 Where priming hurts
11.3.3 Where priming helps
11.4 Combining Human and Machine Intelligence
11.4.1 Annotator Feedback
11.4.2 Maximizing Objectivity by asking what other people would annotate
11.4.3 Recasting continuous problems as ranking problems
11.5 Smart interfaces for maximizing human intelligence
11.5.1 Smart Interfaces for Semantic Segmentation
11.5.2 Smart Interfaces for Object Detection
11.5.3 Smart Interfaces for Language Generation
11.5.4 Smart Interfaces for Sequence Labeling
11.6 Machine Learning to assist human processes
11.6.1 The perception of increased efficiency
11.6.2 Active Learning for increased efficiency