Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction
This work addresses data scarcity for second language learners and educators in SGEC, but it is incremental as it builds on existing end-to-end speech models.
The authors tackled the problem of limited labeled data for spoken grammatical error correction (SGEC) and feedback generation by introducing a pseudo-labeling process that expanded training data from 77 to 2500 hours, leading to improved performance, and they also used prompting with fluent transcriptions to enhance SGEC and feedback results.
Spoken Grammatical Error Correction (SGEC) and Feedback (SGECF) are crucial for second language learners, teachers and test takers. Traditional SGEC systems rely on a cascaded pipeline consisting of an ASR, a module for disfluency detection (DD) and removal and one for GEC. With the rise of end-to-end (E2E) speech foundation models, we investigate their effectiveness in SGEC and feedback generation. This work introduces a pseudo-labelling process to address the challenge of limited labelled data, expanding the training data size from 77 hours to approximately 2500 hours, leading to improved performance. Additionally, we prompt an E2E Whisper-based SGEC model with fluent transcriptions, showing a slight improvement in SGEC performance, with more significant gains in feedback generation. Finally, we assess the impact of increasing model size, revealing that while pseudo-labelled data does not yield performance gain for a larger Whisper model, training with prompts proves beneficial.