Empirical results show that with only character embedding features, the performance of our model outperforms CNN-CNN-MoT, and is close to LSTM-MoT.
there is still a big gap between character embedding and word embedding models, which could come from the fact that we use pretrained word embeddings, which helps improve the performance.