  System combination is an important technique for combining the hypotheses of
different machine translation systems to improve translation performance.
Although early statistical approaches to system combination have been proven
effective in analyzing the consensus between hypotheses, they suffer from the
error propagation problem due to the use of pipelines. While this problem has
been alleviated by end-to-end training of multi-source sequence-to-sequence
models recently, these neural models do not explicitly analyze the relations
between hypotheses and fail to capture their agreement because the attention to
a word in a hypothesis is calculated independently, ignoring the fact that the
word might occur in multiple hypotheses. In this work, we propose an approach
to modeling voting for system combination in machine translation. The basic
idea is to enable words in hypotheses from different systems to vote on words
that are representative and should get involved in the generation process. This
can be done by quantifying the influence of each voter and its preference for
each candidate. Our approach combines the advantages of statistical and neural
methods since it can not only analyze the relations between hypotheses but also
allow for end-to-end training. Experiments show that our approach is capable of
better taking advantage of the consensus between hypotheses and achieves
significant improvements over state-of-the-art baselines on Chinese-English and
English-German machine translation tasks.
