  Grammatical error correction systems improve written communication by
detecting and correcting language mistakes. To help language learners better
understand why the GEC system makes a certain correction, the causes of errors
(evidence words) and the corresponding error types are two key factors. To
enhance GEC systems with explanations, we introduce EXPECT, a large dataset
annotated with evidence words and grammatical error types. We propose several
baselines and analysis to understand this task. Furthermore, human evaluation
verifies our explainable GEC system's explanations can assist second-language
learners in determining whether to accept a correction suggestion and in
understanding the associated grammar rule.
