Poor. The Device has one or more of the following issues: 1. The Device has sustained minor functional damage or product failure that can be easily repaired; 2. One or more of the Device ‘s minor components, peripherals, or accessories is missing or damaged but can be easily repaired or replaced; 3. The Device is missing a majority of original software installation disks or manuals; 4. The Device has more than normal visible wear and tear, including, but not limited to, cracks, dents, scratches, dirt and user-added stickers. If TechForward grades the Device as Poor, the Buyback Amount will be adjusted down by 50%.
Poor. The information in the target was unclear and/or incorrect; reading the source would be necessary for understanding. We aim to reach “complete” scores in mathematics and museum translation, and “useful” scores in patent translation. Dimensions not mentioned in the TAUS scoring are “grammaticality” and “naturalness” of the produced text. The grammar-based method of MOLTO will by definition guarantee grammaticality; failures in this will be fixed by fixing the grammars. Some naturalness will be achieved in the sense of “idiomaticity”: the compile-time transfer technique presented in Section 1.2.3 will guarantee that forms of expression which are idiomatic for the domain are followed. The higher levels of text fluency reachable by Natural Language Generation techniques such as aggregation and referring expression selection have been studied in some earlier GF projects, such as (Xxxxx and Johannisson 2005). Some of these techniques will be applied in the mathematics and cultural heritage case studies, but the main focus is just on rendering information correctly. On all these measures, we expect to achieve significant improvements in comparison to the available translation tools, when dealing with in-grammar input. The new tasks aim at progress beyond the state of the art with the following outcomes: multilingual wiki: from low-quality translation (or hand-made translations) to truly automatic high-quality immediate updates of content. Existing wikis can provide multilingual content only as a result of major additional human effort, and there is no guarantee that the different language versions of the same article are semantically equivalent. controlled languages: from monolingual CNL systems to multilingual ones, where the involved languages can be translated into each other in a meaning-preserving way. Existing CNL systems are monolingual (usually English) and thus cannot be used by people not skilled in that language. interactive systems: from monolingual (or hand-translated or overly simplified machine-translated) systems to multilingual ones. Be Informed developed recently a first prototype of generation of textual explanations without the use of NLG technology. Based on that experience (xxx Xxxxxxxxx, X. et al., 2010), the MOLTO Enlarged EU work is expected to overcome some of the challenges that pattern based sentence generation alone cannot handle, or that take a lot of manual work to address. These include: dealing with morphology aspects, such as, ve...
Poor. The asset is approaching end of service life; condition below standard and a large portion of system exhibits significant deterioration. Increasing potential of affecting service;
Poor. The contractor lacked a basic understanding of the work and the capacity to fulfill the requirements. Contractor’s performance was inadequate and contract requirements were often not met.
Poor. The information in the target was unclear and/or incorrect; reading the source would be necessary for understanding. We aim to reach “complete” scores in mathematics and museum translation, and “useful” scores in patent translation. 31 xxx.xxxxxxxxxx.xxx 32 xxx.xxxxxxxxxxxxxx.xxx 33 xxxx://xxx.xxx.xxx.xxx/ nlp/IQMT/ Dimensions not mentioned in the TAUS scoring are “grammaticality” and “naturalness” of the produced text. The grammar-based method of MOLTO will by definition guarantee grammaticality; failures in this will be fixed by fixing the grammars. Some naturalness will be achieved in the sense of “idiomaticity”: the compile-time transfer technique presented in Section 1.2.3 will guarantee that forms of expression which are idiomatic for the domain are followed. The higher levels of text fluency reachable by Natural Language Generation techniques such as aggregation and referring expression selection have been studied in some earlier GF projects, such as (Xxxxx and Johannisson 2005). Some of these techniques will be applied in the mathematics and cultural heritage case studies, but the main focus is just on rendering information correctly. On all these measures, we expect to achieve significant improvements in comparison to the available translation tools, when dealing with in-grammar input.
Poor. The right to vote in the United States is granted to individuals of what age? A. 18 C. 17 B. 16 D. 19
Poor indicates the component will need repair or replacement now or in the very near future.
Poor. The information in the target was unclear and/or incorrect; reading the source would be necessary for understanding. We aim to reach ―complete‖ scores in mathematics and museum translation, and ―useful‖ scores in patent translation. Dimensions not mentioned in the TAUS scoring are ―grammaticality‖ and ―naturalness‖ of the produced text. The grammar-based method of MOLTO will by definition guarantee grammaticality; failures in this will be fixed by fixing the grammars. Some naturalness will be achieved in the sense of ―idiomaticity‖: the compile-time transfer technique presented in Section
Poor. The criterion is inadequately addressed, Offeror demonstrates only a slight ability to comply, or there are serious inherent weaknesses.
