# Text Normalization Using Encoder-Decoder Networks Based on the Causal Feature Extractor
## 3 Experimental Results
### Analysis of the Types of Errors  
After analyzing the errors made by the proposed CFE method, we have observed that most of these errors can be classified into a reduced set of types. To get a better understanding of these types, all the incorrect predictions of CFE for the test set of the third experiment, E3, were dumped and classified by hand. Based on these observations, the taxonomy of error types has been defined as follows:  
* T1. Infinite loop errors. The attention system of the model gets stuck and the maximum number of printed characters is reached. For example:
* _[PERSON], [PERSON]; [PERSON]; [PERSON], [PERSON]. (2004)._
* _[PERSON], [PERSON]; [PERSON], [PERSON], s; [PERSON], [PERSON] (two thousand four)._
* _[PERSON], [PERSON] e; [PERSON], [PERSON], [PERSON] R s,..................._
* T2. Coincidental errors. Predictions where only a few isolated characters are wrongly printed. For example:
* _The income was $11,091._
* _The income was eleven thousand ninety one dollars._
* T3. Early stop errors. Errors where the model finishes before processing the whole input. For example: *  
Figure 13: Attention matrices obtained by the four models for the sample selected from E2, shown in Table 10. The input sentence (horizontal axis) is “_Belpiela is a community in Tamale Metropolitan District in the Northern Region of Ghana.”_ The values represented are the attention coefficients, \(\alpha_{i}^{t}\) (see Equation (2)); a darker color represents a larger value. \begin{tabular}{r l l l l} Input & _[PERSON], [PERSON] (1 May 2000)._ & _[PERSON], [PERSON] (the first of may two thousand)._ \\ Output & _[PERSON], [PERSON] (the first of may two thousand)._ & _[PERSON], [PERSON] (._ \\ \end{tabular} 
* T4. (Finite) jumps. The attention model finds the same pattern in the entry and repeats/oversees a part of it. For example: \begin{tabular}{r l l} Input & _According to the 2011 census of India, Bhisenagar has 818 households._ \\ Output & _According to the twenty eleven census of India, Bhisenagar has eight hundred eighteen households._ \\ Prediction & _According to the twenty eleven census of India, Bhisenagar has eighteen households._ \\ \end{tabular}   
An automatic classification tool has been implemented in order to (approximately) quantify the errors according to their type. The results are shown in Table 11 where _Others_ refers to the errors unclassified by the tool. Note that errors produced by jumps are not detected by the tool, since they can have different forms, but they represent a big portion of the unclassified errors. As shown, more than half of the errors are produced by infinite loops. These are caused by a malfunction of the attention mechanism, that returns to a previously treated character. The same reason could be inferred for the errors of type T3, but these represent only a 0.12%. On the other hand, the errors of type T2 are most probably due to deficiencies in the decoder. Moreover, the category Others can be due to any component of the system. Besides these types of errors, it is worth-mentioning that some observed errors were caused by the dataset itself, which contains some examples whose expected output is debatable (or simply incorrect). This was also observed by [PERSON] and [PERSON] in [PERSON] and [PERSON] (2018), who estimated this error in a 0.1% of the total (although it was done in a manual analysis of only 1000 samples). These come from different sources, for example, from inconsistent rules for normalizing text among different entries, such as spelling or not spelling an acronym: \begin{tabular}{r l l l} Input & _Upsala: Sprak och folkminnesinstitutet (SOFI)._ \\ Output & _Upsala: Sprak och folkminnesinstitutet (SOFI)._ \\ Prediction & _Upsala: Sprak och folkminnesinstitutet (S \(o\) f i)._ \\ Input & _Chlororformic acid has the formula CICO 2 H._ \\ Output & _Chlororformic acid has the formula c \(l\) c \(o\) two H._ \\ Prediction & _Chlororformic acid has the formula CICO two H._ \\ \end{tabular}