Automatic Grammatical Error Detection of Non-native Spoken Learner English

Abstract

Automatic language assessment and learning systems are required to support the global growth in English language learning. They need to be able to provide reliable and meaningful feedback to help learners develop their skills. This paper considers the question of detecting grammatical errors in non-native spoken English as a first step to providing feedback on a learner's use of the language. A state-of-the-art deep learning based grammatical error detection (GED) system designed for written texts is investigated on free speaking tasks across the full range of proficiency grades with a mix of first languages (L1s). This presents a number of challenges. Free speech contains disfluencies that disrupt the spoken language flow but are not grammatical errors. The lower the level of the learner the more these both will occur which makes the underlying task of automatic transcription harder. The baseline written GED system is seen to perform less well on manually transcribed spoken language. When the GED model is fine-tuned to free speech data from the target domain the spoken system is able to match the written performance. Given the current state-of-the-art in ASR, however, and the ability to detect disfluencies grammatical error feedback from automated transcriptions remains a challenge.This paper reports on research supported by Cambridge Assessment, University of Cambridge. Thanks to Cambridge English Language Assessment for supporting this research and providing access to the BULATS dat