Towards Recognizing Phrase Translation Processes: Experiments on English-French
Table 2: Binary classification (balanced distribution)
['Architecture', 'Accuracy', 'Micro-F1', 'Macro-F1']
The length of our phrases is usually short, especially for word-for-word Literal instances. In order to build a more robust alignment matrix and to avoid the out-of-vocabulary problem, we finally choose to use character embeddings. All the models have been trained in 200 epochs, with a learning rate of 0.0001 using Adam optimizer and the minibatch size of 20. Dropout has been applied to all layers except the output and embedding layers. For the binary classification, Non_literal (NL) class has in total 1127 instances, and 1127 Literal (L) instances are randomly chosen. Besides the preprocessing steps of lowercasing and correcting minor spelling errors, for the neural classifiers, we also normalized the clitic forms to complete words (e.g. ’re → are), and normalized digits to letter form (e.g. 42 → four two). The architecture using word embeddings and \glsmlp obtain better results and is faster than the other two architectures. However, the current data set is too small for neural architectures to produce satisfactory results.