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Automatic Grammatical Error Detection of Non-native Spoken Learner English

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Knill, KM 
Gales, MJF 
Manakul, PP 
Caines, AP 

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.

Description

Keywords

47 Language, Communication and Culture, 4703 Language Studies, 4704 Linguistics, Machine Learning and Artificial Intelligence, 4 Quality Education

Journal Title

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Conference Name

2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Journal ISSN

1520-6149

Volume Title

2019-May

Publisher

IEEE

Rights

All rights reserved
Sponsorship
Cambridge Assessment (unknown)
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 data