NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining
Table 1: Official submission results in different measures
['[BOLD] task', '[BOLD] submission', '[BOLD] layers', '[BOLD] model', '[BOLD] micro F1', '[BOLD] macro F1', '[BOLD] binary F1']
For every setup, we used ELMo word representations as an embedding layer, different setups of dropout in each layer of neural network in the range from 0.3 to 0.6. Each LSTM layer has its hidden size set to 1024 units per layer. For some submissions, we also experimented with model ensemble. We took different number of models, which had the best performance on development (trial) set and used averaging predicted probabilities to get final prediction or voting mechanism and get label with more votes. In subtask A, we used 5 best trained models for voting model ensemble and 3 models for mean model ensemble. In subtask B, we used 3 best trained models for model ensemble. As an official results, binary F1 measure was taken. From these results, we can observe that model ensemble can significantly help obtain better results for both subtasks.