Alpha-2 agonists for sedation of mechanically ventilated adults in intensive care units : a systematic review

Funding The National Institute for Health Research Health Technology Assessment programme. The Health Services Research Unit is core funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates.Peer reviewedPublisher PD

Pushdown automata in statistical machine translation

This article describes the use of pushdown automata (PDA) in the context of statistical machine translation and alignment under a synchronous context-free grammar. We use PDAs to compactly represent the space of candidate translations generated by the grammar when applied to an input sentence. General-purpose PDA algorithms for replacement, composition, shortest path, and expansion are presented. We describe HiPDT, a hierarchical phrase-based decoder using the PDA representation and these algorithms. We contrast the complexity of this decoder with a decoder based on a finite state automata representation, showing that PDAs provide a more suitable framework to achieve exact decoding for larger synchronous context-free grammars and smaller language models. We assess this experimentally on a large-scale Chinese-to-English alignment and translation task. In translation, we propose a two-pass decoding strategy involving a weaker language model in the first-pass to address the results of PDA complexity analysis. We study in depth the experimental conditions and tradeoffs in which HiPDT can achieve state-of-the-art performance for large-scale SMT. </jats:p

Lattice Rescoring Methods for Statistical Machine Translation

This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. It has not been submitted in whole or in part for a degree at any other university. Some of the work has been published previously in conference proceedings (Blackwood et al., 2008a; Blackwood et al., 2008b; Blackwood et al., 2009; Kurimo et al. (2009)) and a journal article (de Gispert et al., 2010), or accepted for publication in forthcoming conference proceedings (Blackwood and Byrne, 2010). The length of this thesis including appendices, references, footnotes, tables and equations is approximately 53,000 words and it contains 56 figures and 58 tables. i Summary Modern statistical machine translation (SMT) systems include multiple interrelated components, statistical models, and processes. Translation is often factored as a cascaded series of modules such that the output of one module serves as the input to the next; this is the SMT pipeline. Simplifying assumptions, limited training data, and pruning during search mean that the maximum likelihood hypothesis may not represent the best translation. Since any errors will be propagated through the SMT pipeline, it is better to avoid hard decisions b

Phrasal segmentation models for statistical machine translation

Phrasal segmentation models define a mapping from the words of a sentence to sequences of translatable phrases. We discuss the estimation of these models from large quantities of monolingual training text and describe their realization as weighted finite state transducers for incorporation into phrase-based statistical machine translation systems. Results are reported on the NIST Arabic-English translation tasks showing significant complementary gains in BLEU score with large 5-gram and 6-gram language models.