Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation
Table 3: CDNMT∗ is our implementation of CDNMT. m and o indicates that the base model is extended with the morphology table and the additional output channel, respectively. mo is the combination of both the extensions. The improvement provided by the boldfaced number compared to CDNMT∗ is statistically significant according to paired bootstrap re-sampling (Koehn, 2004) with p=0.05.
['[BOLD] Model', '[BOLD] En→De', '[BOLD] En→Ru', '[BOLD] En→Tr']
We report results for the bpe→char setting, which means the source token is a bpe unit and the decoder samples a character at each time step. CDNMT is the baseline model. To make our work comparable and show the impact of the new architecture, we tried to replicate CDNMT’s results in our experimental setting, we kept everything (parameters, iterations, epochs etc.) unchanged and evaluated the extended model in the same setting. The morphology table yields significant improvements for all languages and settings. The morphology table boosts the En–Tr engine more than others and we think this is because of the nature of the language. Turkish is an agglutinative language in which morphemes are clearly separable from each other, but in German and Russian morphological transformations rely more on fusional operations rather than agglutination. It seems that there is a direct relation between the size of the morphology table and the gain provided for the decoder, because Russian and Turkish have bigger tables and benefit from the table more than German which has fewer affixes. The auxiliary output channel is even more useful than the morphology table for all settings but En–Ru, and we think this is because of the morpheme-per-word ratio in Russian. The number of morphemes attached to a Russian word is usually more than those of German and Turkish words in our corpora, and it makes the prediction harder for the classifier (the more the number of suffixes attached to a word, the harder the classification task). The combination of the morphology table and the extra output channel provides the best result for all languages.