Non-Autoregressive Grammatical Error Correction Toward a Writing Support System (2020)

Abstract: There are several problems in applying grammatical error correction (GEC) to a writing support system. One of them is the handling of sentences in the middle of the input. Till date, the performance of GEC for incomplete sentences is not well-known. Hence, we analyze the performance of each model for incomplete sentences. Another problem is the correction speed. When the speed is slow, the usability of the system is limited, and the user experience is degraded. Therefore, in this study, we also focus on the non-autoregressive (NAR) model, which is a widely studied fast decoding method. We perform GEC in Japanese with traditional autoregressive and recent NAR models and analyze their accuracy and speed.

## introduction grammatical error correction (gec) is a writing support method for language learners. in recent years, neural gec has been actively researched owing to its ability to produce fluent text. for example, in @xcite , state-of-the-art correction accuracy was achieved by using a transformer @xcite , which is a powerful neural machine translation (nmt) model. because the neural model can see the entire sequence, it can correct errors with long-range dependencies; these errors cannot be corrected by a statistical method that uses n-grams. however, considering the application of gec in a writing support system, we must consider how to handle incomplete sentences. it is easy to present the gec result when the user finishes writing a sentence. however, in case of an incomplete sentence, the user will not know how to fix the sentence while writing it. if the system can perform gec correctly for incomplete sentences, the results can be presented to the user. in most previ-ous studies, complete sentences have been evaluated, and the performance of gec for incomplete sentences has not been researched. in addition, there is a problem that inference speed is slow in a conventional autoregressive (ar) decoder of a sequence-to-sequence model. considering the application of gec in a writing support system, a slower inference speed would restrict its utility or lower the usability of the model. in @xcite , a non-autoregressive (nar) decoder that speeds up inference time by outputting all tokens simultaneously was proposed. following the success of nar models, in @xcite , levenshtein transformer, an nar nmt model that iteratively deletes and inserts inputs, was proposed. its usefulness was verified in machine translation and document summarization tasks. moreover, fast gec methods with sequence tagging using an nar model have been proposed. in @xcite , gec was regarded as a local sequence conversion task, and high-speed gec was achieved by using an nar model that iteratively adapted editing tags in parallel. in @xcite , nar gec was performed by repetitive tagging of editing operations on each token of an input sentence, and higher correction accuracy and faster correction speed than in previous studies were achieved. however, these methods exhibited good performance by narrowing down the target language to english and preparing the editing operations as tags using language knowledge in advance. in this study, we focus on the nar model as a method for high-speed gec. we perform gec in japanese using the nar model that does not need to prepare editing operations in advance. we analyze the proposed method considering its application to writing support systems. in particular, we analyze the relationship between the correction accuracy and the inference speed, focusing on incomplete sentences, and evaluate the impact of hyperparameters on nar models. the contributions of this study can be summarized as follows. ## proposed method this study aims to analyze the effectiveness of an nar model for japanese gec in terms of accuracy and speed, assuming that nar is used as a back-end of a writing support system. ## conclusion in this study, we investigated the applicability of the nar model, which has a constant inference speed, to japanese gec toward constructing a writing support system. the experiments showed that the nar model can obtain a correction accuracy that is equal to or better than that of the ar multilayer convolutional neural model. furthermore, we demonstrated that the gec model trained on complete sentences can also be applied to incomplete sentences. however, we found that when the number of input words is small, the correction accuracy is significantly lower than that of the complete sentence. therefore, the system should defer presenting correction results for short sentences. we also showed that the worst inference time could be reduced by approximately 6.0 seconds, and the average inference time could be reduced by approximately 0.3 seconds in the nar model that performs one-time iterative refinement compared with the ar model. future work includes an extrinsic evaluation of the gec system integrated into a writing support system. moreover, we plan to investigate a largescale pretrained model to improve gec's performance.