Transformer-Based Real-Word Spelling Error Feedback with Configurable Confusion Sets

Real-word spelling errors (RWSEs) pose special challenges for detection methods, as they 'hide' in the form of another existing word and in many cases even fit in syntactically. We present a modern Transformer-based implementation of earlier probabilistic methods based on confusion sets and show that RWSEs can be detected with a good balance between missing errors and raising too many false alarms. The confusion sets are dynamically configurable, allowing teachers to easily adjust which errors trigger feedback.