Modeling Voting for System Combination in Machine Translation
Table 3: Results on the English-German task. The evaluation metric is case-sensitive tokenized BLEU. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer{}_{\mathrm{big}}-fb” (p<0.01). “‡‡”: significantly better than “Jane” (p<0.01). “**”: significantly better than “Hier” (p<0.01).
['Method', 'newstest2014']
Our approach also achieves significant improvements over the state-of-the-art results (p<0.01). The gaps are relatively smaller than those on Chinese-English because the English-German task uses single reference while Chinese-English uses four references. Considering that Transformer{}_{\mathrm{big}}-fb is nowadays acknowledged strongest single system result, Jane cannot improve the translation quality and Hier improves a little while our approach achieves significant improvements, indicating that voting mechanism and end-to-end training of neural networks is important especially when the translations of single systems already have high quality.