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TECHNOLOGY AND TRANSLATIONhave a high statistical probability. The (target-)language model is the setof probabilities of the relative ordering of a given set of TL words. Thesetwo models are then used in conjunction by a so-called decoder, whose task‘consists of applying the translation model to a given sentence S to producea set of probable [TL] words, and then applying the language model to thosewords to produce the target sentence T’ (ibid.), such that the probability ofT is the highest possible. Recent approaches include phrases in both models,with improved results.The main challenge for RBMT is ambiguity at any linguistic level (Arnoldet al. 1994; Arnold 2003), hence the attraction of controlled languages.For SMT the main challenge is data sparsity – words in the current sourcetext which have been encountered only rarely (or even not at all) in thetraining data. Of course, the corrections made to SMT output by the translatorare added to the TM so that the available training data constantlyimproves. The hunger for more data to train SMT systems has encouragedmajor content publishers, particularly in IT, to pool their hithertojealously guarded TM assets within the TAUS Data Association created by theTranslation Automation Users Society (http://www.translationautomation.com).Yet SMT systems are prone to run into difficulties when used on datadifferent from that on which they were trained – to translate email correspondencerather than technical reports, for example. In these circumstancesRBMT systems are judged more robust in maintaining their translationquality. While SMT errors may more often be unfathomable, the errors madeby RBMT systems tend to be more consistent and, as a result, easier for posteditorsto find since they are the product of a rule-based process. For example,a given MT system might regularly insert the definite article before abstractnouns (‘the love conquers all’) when translating from Romance languages.Similarly, instructional steps beginning You must in an English manual mightbe more appropriately translated into many languages by an impersonal constructionsuch as it is necessary to. Specialized companies are already offeringpost-editing services to other LSPs. MT tools are rarely good at supportingthe post-editing process but this new niche market may drive the developmentof better technologies (Allen 2003).7.6 PROJECT MANAGEMENT TOOLSMany translators find a spreadsheet or a generic project managementapplication perfectly adequate for planning and managing their workflows.But, in response to the technological and human complexity of larger projects,specialist translation management tools have appeared. They cover every stepfrom costing and quoting to invoicing. They interface with TM tools to be ableto import the results of source text analysis – word counts for each category ofmatch – and come populated with features peculiar to translation, such as settingrates for defined roles (translator, reviser, reviewer, etc.), SL–TL pairs or123