  In this paper, we present neural model architecture submitted to the
SemEval-2019 Task 9 competition: "Suggestion Mining from Online Reviews and
Forums". We participated in both subtasks for domain specific and also
cross-domain suggestion mining. We proposed a recurrent neural network
architecture that employs Bi-LSTM layers and also self-attention mechanism. Our
architecture tries to encode words via word representations using ELMo and
ensembles multiple models to achieve better results. We performed experiments
with different setups of our proposed model involving weighting of prediction
classes for loss function. Our best model achieved in official test evaluation
score of 0.6816 for subtask A and 0.6850 for subtask B. In official results, we
achieved 12th and 10th place in subtasks A and B, respectively.
