Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks
Table IV: Results on the test set of scenario 1
['Model', '+ operators SER', '+ operators TER', '- operators SER', '- operators TER']
In order to implement the case where there is no composition operators in the input, for the LSTM, biLSTM, and CNN models, the operators were removed from the input sequences. For the treeLSTM model, all the V terms were removed from the equation of the inner nodes. We searched the best hyperparameters for each of the model using the development set of scenario 1. We picked scenario 1 because it is the most common way to split data into training/validation/test sets, i.e. standard split. It can be seen that the composition operators do provide salient information for the task since taking them out results in worse performance across all models (as reflected by increases in error rates). However, the treeLSTM performance does not degrade by much, it is more certain that treeLSTM learns to compose the sub-units chiefly from the tree structure.