An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
Table 11: Accuracy (%) with different BPE vocabulary sizes. “0” represents the character-level models.
['[BOLD] Model', '[BOLD] BPE-size', '[BOLD] English', '[BOLD] German', '[BOLD] Hungarian', '[BOLD] Icelandic', '[BOLD] Swedish']
Hypothesis 4, finally, states that subword-level models are better than character-level models. Our experimental results of BPE-Soft models in four languages (except Swedish) show that subword-level models are superior to character-level models when the BPE vocabulary is small. In subword-level models, the vocabulary includes all the characters and learned subword units. We try different BPE vocabulary sizes. All the subword-level models are trained on LSTMs. In German, Icelandic, and Swedish where the data size is small, the subword-level models surpass character-level models. However, the subword-level models in English and Hungarian are clearly not as well as character-level models.