On Assessing and Developing Spoken ’Grammatical Error Correction’ Systems

Spoken 'grammatical error correction' (SGEC) is an important process to provide feedback for second language learning. Due to a lack of end-to-end training data, SGEC is often implemented as a cascaded, modular system, consisting of speech recognition, disfluency removal, and grammatical error correction (GEC). This cascaded structure enables efficient use of training data for each module. It is, however, difficult to compare and evaluate the performance of individual modules as preceeding modules may introduce errors. For example the GEC module input depends on the output of nonnative speech recognition and disfluency detection, both challenging tasks for learner data. This paper focuses on the assessment and development of SGEC systems. We first discuss metrics for evaluating SGEC, both individual modules and the overall system. The systemlevel metrics enable tuning for optimal system performance. A known issue in cascaded systems is error propagation between modules.