An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction

Abstract

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choices are investigated through extensive experiments, and state-of-the-art performance is achieved on the CoNLL-2014 test set ($F_{0.5}=65.0$) and the official test set of the BEA-2019 shared task ($F_{0.5}=70.2$) without making any modifications to the model architecture.

arXiv commentProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP 2019)

References in corpus (3)

• Sequence to Sequence Learning with Neural Networks
• A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction
• GLEU Without Tuning

Cited by in corpus (6)

• A Comprehensive Survey of Grammar Error Correction
• Improving Readability for Automatic Speech Recognition Transcription
• Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
• Improving Grammatical Error Correction with Machine Translation Pairs
• Parallel Data Augmentation for Formality Style Transfer
• Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction