1 Introduction
Table 5: Accuracy comparison of classification and scoring losses.
['[EMPTY]', '[BOLD] Classification', '[BOLD] Scoring']
The model assigns Good samples a score higher than those to Loose which should have been higher than those assigned to Bad. We changed the final fully-connected layer of the model to give only one output followed by a sigmoid to bound the score in the range [0,1]. We can see that the classification loss outperformed the scoring loss by approximately 4% for each language.