Multilingual Modal Sense Classification using a Convolutional Neural Network
Table 5: Average accuracy on EPOSG.
['[EMPTY]', 'dürfen', 'können', 'müssen', 'sollen', 'micro']
The CNN outperforms both baselines by large margins, per modal verb and in terms of micro average. Given we employed perfectly balanced training data, the classifier performances reflect their ability to learn characteristic information for the classes. Indeed, the NN has great difficulties distinguishing the senses for können (3 senses) and sollen, and is outperformed by CNN-G by +35.6 and +24.4 pp. gains. The confusion matrices for CNN-G show a clear separation of these classes, in contrast to the NN.