  Beam search is the most widely used decoding method for neural machine
translation (NMT). In practice, the top-1 candidate with the highest
log-probability among the n candidates is selected as the preferred one.
However, this top-1 candidate may not be the best overall translation among the
n-best list. Recently, Minimum Bayes Risk (MBR) decoding has been proposed to
improve the quality for NMT, which seeks for a consensus translation that is
closest on average to other candidates from the n-best list. We argue that MBR
still suffers from the following problems: The utility function only considers
the lexical-level similarity between candidates; The expected utility considers
the entire n-best list which is time-consuming and inadequate candidates in the
tail list may hurt the performance; Only the relationship between candidates is
considered. To solve these issues, we design a regularized MBR reranking
framework (RMBR), which considers semantic-based similarity and computes the
expected utility for each candidate by truncating the list. We expect the
proposed framework to further consider the translation quality and model
uncertainty of each candidate. Thus the proposed quality regularizer and
uncertainty regularizer are incorporated into the framework. Extensive
experiments on multiple translation tasks demonstrate the effectiveness of our
method.
