An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
Table 10: The accuracy of different models in Swedish with different dataset settings.
['Training', 'Development', 'Test', 'Att-RNN', 'Att-GRU', 'Att-LSTM', 'Transformer']
As there is much more test data in Swedish compared to other languages, we test our hypothesis by moving some token pairs from test sets to training sets and development sets. More specifically, we create two new datasets, in which 27,000 and 30,000 token pairs are moved from the beginning of the test set to the training set and the development set, respectively. This result indicates that the performance of NMT models is highly related to the size of training set.