[PDF] Top 20 Hypothesis Mixture Decoding for Statistical Machine Translation

... collaborative decoding, an approach that combines translation systems by re-ranking partial and full translations iteratively using n-gram features from the predictions of other member ...during ... See full document

... The pre-sorting during phrase matching has two effects on the search algorithm. Firstly, it defines the order in which the hypothesis expansions take place. As higher scoring phrases are considered first, it is ... See full document

... a translation ta- ble partitioning approach that exploits the result of such a reranking to iteratively guide the de- coder to produce new hypotheses that are more relevant to the complex features ...separate ... See full document

... et al., 2003) is a widely known one using small num- ber of features in a maximum-entropy (log-linear) model (Och and Ney, 2002). The features include phrase translation probabilities, lexical probabilities, ... See full document

... and translation fluency for the current state-of-the-art SMT systems based on IBM models are still too low for publication ...stack decoding process, may not strictly follow the natural target language ... See full document

... The purpose of this paper is to describe speed improve- ments to the greedy decoder mentioned above. While ac- ceptably fast for the kind of evaluation used in Germann et al. (2001), namely sentences of up to 20 words, ... See full document

... In this paper, we have presented a decoding pro- cedure for phrase-based SMT that makes it possi- ble to define feature models with cross-sentence de- pendencies. Our algorithm can be combined with DP beam search ... See full document

... chine translation results is to run the optimizer once per system configuration and to draw conclusions about the experimental manipulation from this sin- gle ... See full document

... improve machine translation quality has made rapid progress in recent ...MMMBR decoding me- thod does not generate new ...co- decoding method, in which n-gram agreement and disagreement ... See full document

... malising by length was not effective. However, the stupid-backoff LM has better coverage and the backing-off behaviour is a clue to the presence of disfluency. Similar cues have been observed in ASR analysis (Chase, ... See full document

... good decoding algorithm is critical to the success of any statistical machine translation ...the translation that is most likely according to set of previously learned parameters (and a ... See full document

... To this end, we propose a method that expands all of the submodels into a composition model, re- ducing the ambiguity of the expanded model by the statistics of hypotheses while decoding. First, we explain the ... See full document

... It is easy to determine the optimal (Viterbi) alignment between the source sentence and its translation. In fact, for IBM models 1 and 2, the Viterbi alignment can be computed using a straight forward algorithm in ... 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

... We presented a modified beam search algorithm for an efficient incremental decoder (ID), which will allow translations to be generated incremen- tally for every word typed by a user, instead of waiting for the entire ... See full document

... uses decoding features in a sentence-level pairwise classification approach for Hybrid MT in order to select the best translations out of outputs produced by statistical and rule- based systems, whereas a ... See full document

... The translation process is treated as a noisy chan- nel model, like those used in speech recognition in which there exists e transcribed as f, and a trans- lation is to infer the best e from f in terms of ...a ... See full document

... Table 2: Examples of Correct, Okay, and Incorrect Translations: for each translation, the first line is an input German sentence, the second line is the human made target translation for[r] ... See full document

... Already at (14), we explicitly ruled out translations y having no derivation at all in the hypergraph. However, suppose the hypergraph were very large (thanks to a large or smoothed translation model and weak ... See full document

... existing statistical MT system that is trained without any linguistic ...MBR decoding can be applied to the MT ...of translation loss func- tions from varied linguistic ... See full document