It has no direct competitors and strongly outperforms classical spell-checking or dictionary-based approaches.
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Our simultaneous diacritics restoration and typos correction approach reaches >94% alpha-word accuracy on the 13 languages. Our approach is also able to restore diacritics in words not seen during training with >76% accuracy. The experimental investigation proves that our approach is able to achieve results (>98%) comparable to the previous state-of-the-art, despite being trained less and on fewer data. For a comparison, we perform diacritics restoration on benchmark datasets of 12 languages, with the addition of Lithuanian. However, both of these problems are typically addressed separately: the state-of-the-art diacritics restoration methods do not tolerate other typos, but classical spellcheckers also cannot deal adequately with all the diacritics missing.In this work, we tackle both problems at once by employing the newly-developed universal ByT5 byte-level seq2seq transformer model that requires no language-specific model structures. Restoring diacritics and correcting spelling is important for proper language use and the disambiguation of texts for both humans and downstream algorithms. The results show the proposed model to even out-perform other systems currently in commercial use.ĭue to the fast pace of life and online communications and the prevalence of English and the QWERTY keyboard, people tend to forgo using diacritics, make typographical errors (typos) when typing in other languages. For this paper, we evaluated our model upon these aspects: Performance comparisons to other models and evaluation on overcorrection. Additionally, we discovered that applying the Copy Mechanism not only resolves the problem of overcorrection but even improves it. 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. Thus, ‘translating’ the error sentence to a correct sentence. 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.
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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. The aim of a spelling correction task is to detect spelling errors and automatically correct them.