  In NMT we search for the mode of the model distribution to form predictions.
The mode and other high-probability translations found by beam search have been
shown to often be inadequate in a number of ways. This prevents improving
translation quality through better search, as these idiosyncratic translations
end up selected by the decoding algorithm, a problem known as the beam search
curse. Recently, an approximation to minimum Bayes risk (MBR) decoding has been
proposed as an alternative decision rule that would likely not suffer from the
same problems. We analyse this approximation and establish that it has no
equivalent to the beam search curse. We then design approximations that
decouple the cost of exploration from the cost of robust estimation of expected
utility. This allows for much larger hypothesis spaces, which we show to be
beneficial. We also show that mode-seeking strategies can aid in constructing
compact sets of promising hypotheses and that MBR is effective in identifying
good translations in them. We conduct experiments on three language pairs
varying in amounts of resources available: English into and from German,
Romanian, and Nepali.
