Neural spelling correction: translating incorrect sentences to correct sentences for multimedia

• Published in: Multimedia tools and applications Vol. 80; no. 26-27; pp. 34591 - 34608
• Main Authors: Park, Chanjun, Kim, Kuekyeng, Yang, YeongWook, Kang, Minho, Lim, Heuiseok
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2021; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Error correction; Machine translation; Multimedia; Multimedia Information Systems; Performance assessment; Sentences; Special Purpose and Application-Based Systems; Spelling; Automatic noise generation; Copy mechanism; Korean spelling correction; Transformer; Overcorrection; Neural machine translation
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09148-2

The aim of a spelling correction task is to detect spelling errors and automatically correct them. In this paper we aim to perform the Korean spelling correction task from a machine translation perspective, allowing it to overcome the limitations of cost, time and data. Based on a sequence to sequence model, the model aligns its source sentence with an ‘error filled sentence’ and its target sentence aligned to the correct counter part. Thus, ‘translating’ the error sentence to a correct sentence. For this research, we have also proposed three new data generation methods allowing the creation of multiple spelling correction parallel corpora from just a single monolingual corpus. Additionally, we discovered that applying the Copy Mechanism not only resolves the problem of overcorrection but even improves it. For this paper, we evaluated our model upon these aspects: Performance comparisons to other models and evaluation on overcorrection. The results show the proposed model to even out-perform other systems currently in commercial use.