1 Introduction
Table 14: The macro F1-scores using different methods on the 2018 ILI test set. The results presented for the first time are in bold.
['[BOLD] Method (Team)', '[BOLD] Features used', '[BOLD] F1']
The results of the first shared task on Indo-Aryan language identification are described by Zampieri et al. \citeAvardial2018report. Eight teams submitted results on the task. As for the 2nd edition of the GDI shared task, we participated with an early version of the method described in this article. Seven other teams submitted results on the shared task. In addition to the SVMs, they experimented with Recurrent Neural Networks (RNN) with GRUs and LSTMs but their RNNs never achieved results comparable to the SVMs. In addition to Ridge regression, they experimented with NB and SGD classifiers, which did not perform as well. The team dkosmajac used normalized Euclidean distance. The team we_are_indian used word-level LSTM RNNs in their best submission and statistical n-gram approach with mutual information in their second submission The team LaMa used NB. Based on the results on the development set, we decided to use LM adaptation with 18 iterations on the test set. The test resulted in a macro F1-score of 0.958, which is again almost the same as in the shared task, though this time some small fractions higher. We report the F1-scores obtained by the different teams participating in the ILI 2018 with the results form this article in bold. The methods used are listed in the first column, used features in the second column, and the macro F1-scores in the third column.