  This paper describes our submission for the SemEval-2019 Suggestion Mining
task. A simple Convolutional Neural Network (CNN) classifier with contextual
word representations from a pre-trained language model was used for sentence
classification. The model is trained using tri-training, a semi-supervised
bootstrapping mechanism for labelling unseen data. Tri-training proved to be an
effective technique to accommodate domain shift for cross-domain suggestion
mining (Subtask B) where there is no hand labelled training data. For in-domain
evaluation (Subtask A), we use the same technique to augment the training set.
Our system ranks thirteenth in Subtask A with an $F_1$-score of 68.07 and third
in Subtask B with an $F_1$-score of 81.94.
