Realistic Grammar Error Simulation using Markov Logic

The development of Dialog-Based Computer-Assisted Language Learning (DB-CALL) systems requires research on the simulation of language learners. This paper presents a new method for generation of grammar errors, an important part of the language learner simulator. Realistic errors are generated via Markov Logic, which provides an effective way to merge a statistical approach with expert knowledge about the grammar error characteristics of language learners. Results suggest that the distribution of simulated grammar errors generated by the proposed model is similar to that of real learners. Human judges also gave consistently close judgments on the quality of the real and simulated grammar errors.