Grammatical Error Correction with Denoising Autoencoder

Abstract

A denoising autoencoder sequence-to-sequence model based on transformer architecture proved to be useful for underlying tasks such as summarization, machine translation, or question answering. This paper investigates the possibilities of using this model type for grammatical error correction and introduces a novel method of remark-based model checkpoint output combining. This approach was evaluated by the BEA 2019 shared task. It was able to achieve state-of-the-art F-score results on the test set 73.90 and development set 56.58. This was done without any GEC-specific pre-training, but only by fine-tuning the autoencoder model and combining checkpoint outputs. This proves that an efficient model solving GEC might be trained in a matter of hours using a single GPU.

Analyzing the Application of Multimedia Technology Assisted English Grammar Teaching in Colleges

“…In response to this problem, a large number of studies have been proposed by scholars and experts. Krzysztof pajak et al proposed a seq2seq error correction method based on denoising automatic coding and obtained an F1 score of 56.58% [4]. Zhaoquan Qiu proposed a two-stage Chinese grammar error correction algorithm, which is characterized by providing reference for Chinese grammar error correction and introducing a deep learning algorithm [5]; Ailani Sagar summarizes the current mainstream syntax error correction algorithms, which provide a reference for a comprehensive understanding of syntax error correction [6].…”