[PDF] Top 20 Active Learning for Statistical Phrase based Machine Translation

... (Dasgupta and Hsu, 2008) propose a technique for sample selection that, under certain settings, is guar- anteed to be no worse than random sampling. Their method exploits the cluster structure (if there is any) in the ... See full document

... The results of the previous sections were all based on experiments on the BTEC corpus. This corpus is unsual in that the sentences are short and the training data size is also small. In order to establish that our ... See full document

... of translation derivations in phrase-based statistical ma- chine translation is given by the intersec- tion between a translation lattice and a tar- get language ... See full document

... direct phrase probabilities adaptation as opposed to ...the phrase level, assigning each phrase pair a weight according to its relevance to the test ...compare phrase weighting to a “flat” ... See full document

... In statistical machine translation, the gen- eration of a translation hypothesis is com- putationally ...for phrase-based statistical machine trans- lation, namely ... See full document

... the translation probabilities of connecting phrases from out-of-domain corpus are estimated by real corpus ...their translation probabil- ities, which may be useful in decoding, into in-domain bilingual ... See full document

... the phrase level, these models typically have a simple distortion model that reorders phrases in- dependently of their content (Och and Ney, 2004; Koehn et ... See full document

... rent statistical machine translations are per- formed on phrases, which are considered as translation ...the phrase predicted by a language model. To build the new phrase-based ... See full document

... 1+2+3+4+5+6+7 28.7 23.8 55.8 53.7 Moses (1+. . .+7) 28.9 23.5 55.8 53.6 Table 6: French-to-English results for development and test data according to different log-linear scenarios. These models can also be implemented ... See full document

... An efficient decoding algorithm is a cru- cial element of any statistical machine translation system. Some researchers have noted certain similarities between SMT decoding and the famous Traveling ... See full document

... and translation decisions in the latter – we intro- duce modified distortion matrices: a novel method to seamlessly provide to the decoder a set of likely long reorderings pre-computed for a given input ... See full document

... The 4th Workshop on South and Southeast Asian NLP WSSANLP, International Joint Conference on Natural Language Processing, pages 72–76, Nagoya, Japan, 14-18 October 2013.... ºï ðòïï ðòðç.[r] ... See full document

... do not correspond to syntactic constituents. Re- stricting phrases to syntactic constituents has been shown to harm performance (Koehn et al., 2003), so we tighten our definition of a violation to disregard cases where ... See full document

... Abstract- Machine Translation (MT) is a technique that automatically translates text from one natural language to another using machine like ...computer. Machine Transliteration (MTn) is also ... See full document

... databases, translation memories, machine translation and hu- man ...a translation memory of in- dividual sentence pairs from previously posted ads; another third of all text is typically found ... See full document

... forward phrase transla- tion probability P ( ¯ t | ¯ s ) . The maximal phrase length was set to seven words, as it is also used during the standard phrase extraction ...7M phrase pairs after ... See full document

... standard phrase- based machine translation decoder that operates according to the same principles as the publicly available PHARAOH (Koehn, 2004) and MOSES (Koehn et ... See full document

... the language model a penalty imposed when formal compostion operators are violated. We combine the n-gram language model with a penalty factor that measures the number of encountered combinatory operator violations in a ... See full document

... hierarchical phrase-based SMT by integrat- ing Tromble and Eisner (2009)’s word reordering model into decoder as a feature, which estimates the probability of any two source words in a sen- tence being ... See full document

... Recall was reasonably high. However, the upper bound of recall was 100 percent because we used a multilingual corpus whose sentences were aligned to each other across all four languages, as described in Section 4.1. ... See full document