1 Introduction
Table 8: The weighted F1-scores using different methods on the 2017 GDI test set. The results from the experiments presented in this article are bolded.
['[BOLD] Method (Team)', '[BOLD] Features used', '[BOLD] wgh. F1']
The results of the first shared task on German dialect identification are described by Zampieri et al. \citeAzampieri9. Ten teams submitted results on the task utilizing a variety of machine learning methods used for language identification. plurality voting , mean probability, and meta-classifier. The meta-classifier ensemble using the Random Forest algorithm for classification obtained the best results. used SVMs as well, and their best results were obtained using an additional procedure to equalize the number of sentences assigned to each category. Team CLUZH experimented with naïve Bayes (NB), conditional random fields (CRF), as well as a majority voting ensemble consisting of NB, CRF, and SVM Their best results were reached using CRF. Team qcri_mit used an ensemble of two SVMs and a stochastic gradient classifier (SGD). Team unibuckernel experimented with different kernels using kernel ridge regression (KRR) and kernel discriminant analysis (KDA) They obtained their best results using KRR based on the sum of three kernels. Team ahaqst used cross entropy (CE) with character and word n-grams We decided to try iterative LM adaptation using 485 epochs with the test set. The tests resulted in a weighted F1-score of 0.700, which was a further 0.013 increase on top of the score obtained without additional iterations. The methods used are listed in the first column, used features in the second column, and the best reached weighted F1-score in the third column. The results from this paper are bolded. The 0.013 point increase obtained with the iterative LM adaptation over the non-iterative version might seem small when compared with the overall increase over the scores of the HeLI 2.0 method, but the increase is still more than the difference between the 1st and 3rd best submitted methods on the original shared task.