Papers by Fabienne Cap
An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization (C18-1)
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| Challenge: | In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages . we find that NMT model is much better than SMT in terms of character error rate . |
| Approach: | They propose to use NMT models to solve the problem of historical spelling normalization in five languages. |
| Outcome: | The proposed method improves historical spelling normalization for five languages. |