1 Introduction
Table 10: Errors distribution from the test set of E3.
['Type', 'T1', 'T2', 'T3', 'Others', 'Total']
A simple classification tool has been implemented in order to (approximately) quantify the errors according to their type. Note that errors produced by jumps are not detected by the tool, but represent a big portion of the unclassified errors. Thus, it can be concluded that, in this case, CFE is preferable to FE due to its qualitative benefits and, to a lesser extent, its quantitative results. These problems make the model lost its focus and jump around when confounding similar parts of the entry. Therefore, this could be greatly improved by using more sophisticated attention models that, for example, focus on local environments, take into account the index, or force the model to put more focus in the next character of the input.