1 Introduction
Table 11: The macro F1-scores using different methods on the 2018 GDI test set. The results from the experiments presented in this article are bolded.
['[BOLD] Method (Team)', '[BOLD] Features used', '[BOLD] F1']
Seven other teams submitted results on the shared task. The team LaMa used a voting ensemble of eight classifiers. The team dkosmajac used normalized Euclidean distance. However, the exact number of included features was not determined using the development set, but it was the optimal number for the test set. Using the full set of features resulted again in a lower score. Based on the results on the development set, we decided to try two different counts of iterations: 738, which is the number of epochs in the middle of the best scores, and 20, after which the results started to fluctuate. The tests resulted in a macro F1-score of 0.696 for 738 epochs and 0.704 for 20 epochs. As an additional experiment, we evaluated the iterative adaptation on a test set, from which the unknown dialects had been removed and obtained an F1-score of 0.729 with 738 epochs. From the results, it is clear that the presence of the unknown language is detrimental to repeated language model adaptation. The methods used are listed in the first column, used features in the second column, and the best reached macro F1-score in the third column.