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: 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 system performance as upstreams modules 
may introduce errors. For example, the GEC module input depends on the 
output of non-native speech recognition and disfluency detection, both 
challenging tasks for learner data. This work focuses on the assessment and de- velopment of SGEC systems. To mitigate error propagation in cascaded SGEC systems, semi-supervised finetuning and self-distillation approaches are investigated. Lastly, confidence based filtering is adopted to improve the feedback quality.

On Assessing and Developing Spoken ’Grammatical Error Correction’ Systems

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## Welcome to NAACL 2022!
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Yiting Lu

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On Assessing and Developing Spoken ’Grammatical Error Correction’ Systems

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