As advances in the field of machine translation (MT) continue to allow for greater distribution of multi-lingual information, an increasing number of browser-based toolkits are being developed in an attempt to empower translators, and decrease the gap between speed, and quality of output. However, these systems so far have failed to satisfy the translation community due to a fundamental misunderstanding of the way translators work. There has been an essential divergence in the study and implementation of machine translation systems-away from the original goal of providing perfect target-language texts, towards an attempt to provide output that can be largely understood for quick dissemination. The realisation that current methodology in the field does not allow for such high quality to be attained has turned researchers' attentions to the development of systems that act as supporting processes during several of the traditional stages of translation. Nonetheless, such efforts have continued on the assumption that high quality translation from the get- go is the most important factor, rather than assessing how translators' work ows react to poor quality target language text generated by such systems. Our aim has been to identify techniques that reduce the time that translators spend on the whole transfer process - by realising that a SMT core can provide much more than a raw translation, even at this relatively early stage in its development. Our research evaluates the shortcomings of existing systems, and proposes a new kind of integrated online toolkit that addresses these limitations based on an under- standing of the most significant and unnecessary struggles between translators and MT systems. Consequently we believe that with just a handful of changes it is pos- sible to envisage an arrangement that will allow translators to regularly outperform non-MT-based working strategies, both in terms of speed and quality.
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