[PDF] Top 20 Fast Decoding and Optimal Decoding for Machine Translation

... the decoding process in speech recognition (SR) and machine translation (MT) is that speech is always pro- duced in the same order as its ...SR decoding there is always a sim- ple ... See full document

... The decoding problem in Statistical Ma- chine Translation (SMT) is a computation- ally hard combinatorial optimization prob- ...the decoding problem. A fam- ily of provably fast ... See full document

... (MBR) decoding ob- jective improves BLEU scores for machine trans- lation output relative to the standard Viterbi ob- jective of maximizing model ...our fast decoding procedure can select ... See full document

... LM score computations are among the most expen- sive in decoding. Delaney et al. (2006) report signif- icant improvements in runtime by removing unnec- essary LM lookups via early pruning. Here we de- scribe an LM ... See full document

... two decoding objectives for two ...evaluating translation systems) and log-likelihood (the most widely used learning criterion for neural machine ...greedy decoding and beam ...greedy ... See full document

... Test data. In this experiment, we use the English-German data released at the WMT’16 APE shared task (Bojar et al., 2016). To anno- tate the test set, instead of relying on automatic quality, predictions, we exploit ... See full document

... appropriate decoding objective. Sev- eral new decoding approaches have been proposed recently that leverage some notion of consensus over the many weighted derivations in a transla- tion ...the fast ... See full document

... We have presented our approach to NMT decod- ing with terminology constraints using decoder at- tentions which enables reduced output duplication and better constraint placement compared to ex- isting methods. Our ... See full document

... chine translation, but one challenge of such models is a significant increase in training and decoding cost compared to phrase-based ...efficient decoding, with a goal of achiev- ing accuracy close ... See full document

... our decoding algorithm moves from a coarse upperbound where every node stores an empty string to a variable-order representation which is sufficient to prove an optimum ... See full document

... the translation forest and the language model by expanding a limited beam of hypotheses from each nonterminal ...a fast-to-compute heuris- tic view of outside weights (cheapest way to com- plete a ... See full document

... malization, but found it performed slightly worse. The merged partial hypotheses can be stored, and by assuming that their future predictions will be the same as their mergers, a lattice-like trans- lation graph can be ... See full document

... The results in Table 4 show that the incremen- tal decoder was significantly faster than the beam search in re-decoding mode almost by a factor of 9 in the best case (for Fr-En). The speedup is pri- marily due to ... See full document

... To the best of our knowledge, Goldberg et. al. (2012) first define the concept of dynamic oracle and propose an online algorithm for parsing problems, , which provides a set of optimal transitions for every valid ... See full document

... phrase-based decoding (Koehn et ...statistical machine translation seeks to find the output sequence, y, that maximizes ˆ the probability of a function parameterized by a model, θ, and an input ... See full document

... which has been used almost exclusively for decod- ing SMT models in the recent literature has very strong assumptions of locality built into it. DP beam search for phrase-based SMT was described by Koehn et al. (2003), ... See full document

... society, translation between related languages is becoming an important ...addition, translation to/from related languages to a lingua franca like English is also very ...statistical machine ... See full document

... English translation task. Our translation model was trained on about 1M parallel sentence pairs (about 28M words in each language), which are sub-sampled from corpora distributed by LDC for the NIST MT ... See full document

... forward translation from the source to the target language, and reverse trans- lation in the target to source ...a translation which scores well un- der both the forward and reverse translation mod- ... See full document

... However, both Model-III + and Distinguishing Model are worse than Koehn-10 at some high fuzzy match intervals. The reason is that the TM factors are trained on the news domain but the test set is from computer technical ... See full document