[PDF] Top 20 Linguistically Annotated BTG for Statistical Machine Translation

... of annotated lexical rules, we la- bel each source phrase x with linguistic annota- tion a through the annotation algorithm given the source-side parse tree, and retrieve x#a from Ω ...features, annotated ... See full document

... manually annotated versions of the Penn Wall Street Journal corpus were used, each pro- viding different annotations over the same ...Czech translation of the Penn Wall Street Journal corpus, provided ... See full document

... There are preordering methods that do not need parse trees. They are usually trained only on auto- matically word-aligned parallel text. It is possible to mine parallel text from the Web (Uszkoreit et al., 2010; Antonova ... See full document

... Linguistic knowledge plays an important role in phrase movement in statistical machine trans- lation. To efficiently incorporate linguistic knowledge into phrase reordering, we propose a new approach: ... See full document

... into statistical models is an everlasting topic in natural language ...chine translation community. Along with the suc- cess of statistical machine translation (SMT) models (summarized ... See full document

... novel statistical machine translation method which employs a BTG-based reordering model during decod- ...ing. BTG-based reordering models for pre- ordering have been widely explored, ... See full document

... use statistical machine translation and morphology information from two different morphological analyzers to try to improve translation quality by linguistically motivated ...Moses ... See full document

... Unsupervised word segmentation (UWS) can provide domain-adaptive segmenta- tion for statistical machine translation (SMT) without annotated data, and bilin- gual UWS can even optimize ... See full document

... our linguistically- augmented statistical machine translation model from Bulgarian to English, which combines a statistical machine translation (SMT) system (as backbone) ... See full document

... Punctuation marks were already used in pars- ing (Christine Doran, 2000) and statistical machine translation (Och et al., 2003). In (Och et al., 2003), three kinds of features are defined, all re- ... See full document

... of Statistical Machine Translation (SMT), and an important factor of its quality and ...control translation complexity appear to be too ...during translation are not discriminative ... See full document

... This paper has presented generalizations of ordinary parsing that emerge when the grammar and/or the input can be multidimensional. Along the way, it has elucidated the relationships between ordinary parsers and other ... See full document

... Although the interaction of these two factors can be quite profound, it may help the reader to think o f the translation probability as suggesting words from the source language t h at m[r] ... See full document

... modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Trans- lation (NMT) systems from monolingual cor- pora only (Artetxe et ... See full document

... We found that the character-level system does not suffer from tokenization error and out-of- vocabulary issue. The JPO corpus contains many technical terms and loanwords like chemical com- pound names, which are more ... See full document

... in statistical parsing: Nivre and McDonald (2008) integrated two models for dependency parsing by letting one model learn from features generated by the other; ...for statistical parsing: i) the diversity ... See full document

... Ensemble Learning is a machine learning par- adigm where multiple learners are trained to solve the same problem. An ensemble is viewed as a collection of learners which are usually called base learners. Base ... See full document

... Nowadays statistical machine translation shows its benefits and has received much ...phrase-based statistical machine translation was carefully ...better. Translation ... See full document

... Tokens are the fundamental textual units in the ALVIS text processing line. This segmentation is not linguistically grounded. It serves no other purpose but to provide a start- ing point from which to implement ... See full document

... Most modern statistical machine translation systems are based on linear statistical models. One extremely effective method for estimating the model parameters is minimum error rate training ... See full document