T emplate GEC : Improving Grammatical Error Correction with Detection Template (2023)

Abstract: Grammatical error correction (GEC) can be divided into sequence-to-edit (Seq2Edit) and sequence-to-sequence (Seq2Seq) frameworks, both of which have their pros and cons. To utilize the strengths and make up for the shortcomings of these frameworks, this paper proposes a novel method, TemplateGEC, which capitalizes on the capabilities of both Seq2Edit and Seq2Seq frameworks in error detection and correction respectively. TemplateGEC utilizes the detection labels from a Seq2Edit model, to construct the template as the input. A Seq2Seq model is employed to enforce consistency between the predictions of different templates by utilizing consistency learning. Experimental results on the Chinese NLPCC18, English BEA19 and CoNLL14 benchmarks show the effectiveness and robustness of TemplateGEC.Further analysis reveals the potential of our method in performing human-in-the-loop GEC. Source code and scripts are available at https://github.com/li-aolong/TemplateGEC .

## introduction grammatical error correction (gec) is a fundamental task in natural language processing that focuses on identifying and correcting grammatical errors in written text @xcite . the utilization of gec is wide-ranging, including but not limited to, improving the quality of machine translation @xcite , increasing the readability of text @xcite , and assisting non-native speakers in language proficiency @xcite . there has been a significant amount of research in the field of gec @xcite @xcite @xcite @xcite , which can be broadly ## templategec this section introduces the proposed method as illustrated in figure 2 . templategec integrates detection information generated by a seq2edit model and fuses the information into a seq2seq model for model enhancement. ## conclusion this paper presents a new method for gec, called templategec, which integrates the seq2edit and seq2seq frameworks, leveraging their strengths in error detection and correction. templategec converts the original erroneous sentence into a novel template format that incorporates predicted and gold error detection labels, which are generated by a seq2edit model. besides, templategec incorporates gold label-assisted consistency learning to enhance performance by maximizing the consistency between the predictions of the predicted and gold templates through the use of a seq2seq model. experimental results on widely-used english and chinese benchmarks show that templategec exhibits competitive performance in comparison to previous gec methods. additional analysis suggests that the proposed method is a promising approach for human-in-the-loop gec and confirms that tem-plategec is effective and robust. we will investigate the feasibility of adapting templategec to other languages and assess its effectiveness through additional experimentation in our future work.