Grammatical error correction based on learner comprehension model in oral conversation

Abstract

We aim to provide grammar error feedback to learners. It is known that grammar error detection and feedback are challenging problems in written language, however, they become much more difficult tasks in oral conversation because it is difficult for a system to judge whether an error is due to grammar or automatic speech recognition (ASR). False alarms occur when a learner correctly utters a remark, but the system gives feedback implying an error. Minimizing the false alarm rate is especially critical in education applications because it is imperative that the tutor give correct instruction to learners. Thus, to reduce the false alarm rate in grammar error detection and feedback, we apply a partially observable Markov decision process (POMDP) when the system provides feedback about a learner's mistake. The POMDP models uncertainty between grammar errors and ASR errors. An additional advantage of our method is that “belief states” in POMDP can be used for learner models which indicate each individual learner's grammar comprehension level.