Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation (2020.lrec-1)
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| Challenge: | a recent study shows that typographical errors are now ubiquitous . traditional spelling correction software is inadequate to correct typographical mistakes . |
| Approach: | They propose to generate typographical errors based on annotated spelling errors . they then use annotations to introduce errors into substantially larger corpora . |
| Outcome: | The proposed method generates typographical errors that require context-aware error detection . it also shows that machine learning can correct typographical mistakes based on the data . |
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