Compositional Representation of Morphologically-Rich Inputfor Neural Machine Translation
Table 3: The performance of different segmentation models trained on the English portion of our benchmark in the Morpho Challenge shared task.
['[BOLD] Method', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1 Score']
The simple NMT model constitutes the baseline in our study and performs translation directly at the level of sub-word units, which can be of four different types: characters, character trigrams, BPE sub-word units, and LMVR sub-word units. The compositional model, on the other hand, performs NMT with input representations composed from sub-lexical vocabulary units. In our study, we evaluate representations composed from character trigrams, BPE, and LMVR units. This choice aids us in evaluating the morphological knowledge contained in input representations in terms of the translation accuracy in NMT.