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In this paper, we present neural model architecture submitted to theSemEval-2019 Task 9 competition: "Suggestion Mining from Online Reviews andForums". We participated in both subtasks for domain specific and alsocross-domain suggestion mining. We proposed a recurrent neural networkarchitecture that employs Bi-LSTM layers and also self-attention mechanism. Ourarchitecture tries to encode words via word representations using ELMo andensembles multiple models to achieve better results. We performed experimentswith different setups of our proposed model involving weighting of predictionclasses for loss function. Our best model achieved in official test evaluationscore of 0.6816 for subtask A and 0.6850 for subtask B. In official results, weachieved 12th and 10th place in subtasks A and B, respectively.

NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining