著者

本間 広樹 小町 守

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.37, no.1, pp.B-L22_1-14, 2022-01-01 (Released:2022-01-01)

参考文献数

33

---

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 GEC 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. Furthermore, in this study, we construct a writing support system with a grammatical error correction function. Specifically, the trained NAR model is embedded in the back-end system. We confirm the system’s effectiveness by both objective and subjective evaluations.