A possible improvement to decoding is to keep a set of good candidates instead of just keeping the best candidate, and to use a better scoring function (re scoring) to rate these good candidates so that we may pick the best one according to this refined score. The set of candidates can be kept either as a list (the N-best list approach) or as a subset of the models (a lattice). Re scoring is usually done by trying to minimize the Bayes risk (or an approximation thereof) Instead of taking the source sentence with maximal probability, we try to take the sentence that minimizes the expectancy of a given loss function with regards to all possible transcriptions (i.e., we take the sentence that minimizes the average distance to other possible sentences weighted by their estimated probability). The loss function is usually the Levenshtein distance, though it can be different distances for specific tasks; the set of possible transcriptions is, of course, pruned to maintain tractability. Efficient algorithms have been devised to re score lattices represented as weighted finite state transducers with edit distances represented themselves as a finite state transducer verifying certain assumptions.
A possible improvement to decoding is to keep a set of good candidates instead of just keeping the best candidate and to use a better scoring function (re scoring) to rate these good candidates so that we may pick the best one according to this refined score The set of candidates can be kept either as a list (the N-best list approach) or as a subset of the models (a lattice) Re scoring is usually done by trying to minimize the Bayes risk (or an approximation thereof) Instead of taking the source sentence with maximal probability we try to take the sentence that minimizes the expectancy of a given loss function with regards to all possible transcriptions (ie we take the sentence that minimizes the average distance to other possible sentences weighted by their estimated probability) The loss function is usually the Levenshtein distance though it can be different distances for specific tasks; the set of possible transcriptions is of course pruned to maintain tractability Efficient algorithms have been devised to re score lattices represented as weighted finite state transducers with edit distances represented themselves as a finite state transducer verifying certain assumptions