Online Incremental Machine Translation

In this thesis we investigate the automatic improvements of statistical machine translation systems at runtime based on user feedback. We also propose a framework to use the proposed algorithms in large scale translation settings

Human Feedback in Statistical Machine Translation

The thesis addresses the challenge of improving Statistical Machine Translation (SMT) systems via feedback given by humans on translation quality. The amount of human feedback available to systems is inherently low due to cost and time limitations. One of our goals is to simulate such information by automatically generating pseudo-human feedback. This is performed using Quality Estimation (QE) models. QE is a technique for predicting the quality of automatic translations without comparing them to oracle (human) translations, traditionally at the sentence or word levels. QE models are trained on a small collection of automatic translations manually labelled for quality, and then can predict the quality of any number of unseen translations. We propose a number of improvements for QE models in order to increase the reliability of pseudo-human feedback. These include strategies to artificially generate instances for settings where QE training data is scarce. We also introduce a new level of granularity for QE: the level of phrases. This level aims to improve the quality of QE predictions by better modelling inter-dependencies among errors at word level, and in ways that are tailored to phrase-based SMT, where the basic unit of translation is a phrase. This can thus facilitate work on incorporating human feedback during the translation process. Finally, we introduce approaches to incorporate pseudo-human feedback in the form of QE predictions in SMT systems. More specifically, we use quality predictions to select the best translation from a number of alternative suggestions produced by SMT systems, and integrate QE predictions into an SMT system decoder in order to guide the translation generation process

Determining predictors of outcome on factors of attention following paediatric arterial ischemic stroke

Attention is a facet of cognition that is responsible for the development of most cognitive processes. Insult to the brain prior to or during the development of attention can be detrimental to various aspects of cognitive development and, as a result, to a child\u27s ability to acquire new knowledge and skills. One example of cerebral insult in childhood is stroke. Given the importance of attention for the development of cognitive skills, identifying the factors of attention is critical to understanding cognitive outcomes in children with stroke. In the present investigation, a three-factor and a four-factor model of attention were tested using confirmatory factor analysis on a set of neuropsychological tests purported to measure various aspects of attention, in order to determine the model of attention best represented by a sample of children with arterial ischemic stroke. It was determined that both a three- and four-factor model of attention fit the data equally well when the same measures were included in both models. Despite similarities between the models, the four-factor model of attention was argued to be the best fit, due to theoretical, neuroanatomical, and developmental considerations. When the four-factor model was used to determine predictors of outcome, both Age at Stroke and Age at Testing were significant predictors of outcome on the Shift and Focus/Execute factors of attention, but not on the Encode and Sustain factors. The findings are discussed within the framework of a vulnerability vs. a plasticity model. Implications for clinical practice are also considered

The development of quantifiable measurements to characterise the epidermal integrity of the equine hoof capsule.

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