Factored Neural Machine Translation
Table 1: Comparison of the performance of the NMT and FNMT systems in terms of %BLEU score evaluating at word level, and separately, each output lemma and factors. The size of the output layer and the size of the corresponding vocabulary are presented in columns 2 and 3. Columns 4 and 5 show coverage in test dataset and number of OOVs, respectively. Last column corresponds to the oracle output.
['[BOLD] Model', '[BOLD] Output  [BOLD] size', '[BOLD] Output  [BOLD] vocab.', '[BOLD] Coverage (%)', '[BOLD] #OOV', '[BOLD] #Par.', '[BOLD] %BLEU  [BOLD] word', '[BOLD] %BLEU  [BOLD] lem.', '[BOLD] %BLEU  [BOLD] fact.', '[BOLD] Oracle  [BOLD] word']
The Factored NMT system aims at integrating linguistic knowledge into the decoder in order to obtain better performance when facing out of domain data and/or a low resource setup. To assess the feasibility and estimate the potential gain of our approach, we performed a set of experiments reducing the output vocabulary size, simulating such an environment. We can see that a potential gain of more than 1.5% BLEU points can be achieved with a perfect modeling of the factors, which is encouraging.