An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
Table 7: The results of combining the NMT-based method and the dictionary-based method. "△" denotes the absolute improvement on accuracy (%) compared to the NMT-based method.
['[EMPTY]', '[BOLD] English Acc', '[BOLD] English △', '[BOLD] German Acc', '[BOLD] German △', '[BOLD] Hungarian Acc', '[BOLD] Hungarian △', '[BOLD] Icelandic Acc', '[BOLD] Icelandic △', '[BOLD] Swedish Acc', '[BOLD] Swedish △']
Therefore, we explore a hybrid method, combining the NMT-based method and the dictionary-based method. More specifically, we first extract a list of unchanged spellings from the training set. During the evaluation, if a word is in this list, we simply copy it as its normalization. If it is not in the list, we feed it to the NMT models. In particular, the improvements on Icelandic are around 5%.