Morphology Generation for Statistical Machine Translationusing Deep Learning Techniques
Table 3: Values of the different parameters of the classifiers
['Parameter', 'Small Num', 'Small Gen', 'Large Num', 'Large Gen']
Regarding classification parameters, experimentation has shown that number and gender classification tasks have different requirements. The best window size is 9 and 7 words for number and gender, respectively. In both cases increasing this size lowers the accuracy of the system. The vocabulary size is fixed as a trade-off between giving enough information to the system to perform the classification while removing enough words to train the classifier for unknown words. The embedding size of 128 results in stable training, while further increasing this value augmented the training time and hardware cost. The filter size in the convolutional layer reached best results when it was slightly smaller than the window size, being 7 and 5 the best values for number and gender classification, respectively. Finally, increasing LSTM nodes up to 70 improved significantly for both classifiers.