This paper describes the process of implementing a machine translation system (MT system) and the problems and pitfalls encountered within this process at CLS Corporate Language Services AG, a language solutions provider for the Swiss financial services industry, in particular UBS AG and Zurich Financial Services. The implementation was based on the perceived requirements of large organizations, which is why the focus was more on practical rather than academic aspects. The paper can be roughly divided into three parts: (1) definition of the implementation process, co-ordination and execution, (2) implementation plan and customer/user management, (3) monitoring of the MT system and related maintenance after going live.

Most large companies are very good at “getting the message out” –publishing reams of announcements and documentation to their employees and customers. More challenging by far is “getting the message in” – ensuring that these messages are read, understood, and acted upon by the recipients. This paper describes NCR Corporation’s experience with the selection and implementation of a machine translation (MT) system in the Global Learning division of Human Resources. The author summarizes NCR‘s vision for the use of MT, the competitive “fly-off” evaluation process he conducted in the spring of 2000, the current MT production environment, and the reactions of the MT users. Although the vision is not yet fulfilled, progress is being made. The author describes NCR’s plans to extend its current MT architecture to provide real-time translation of web pages and other intranet resources.

For over ten years, Ford Vehicle Operations has utilized an Artificial Intelligence (AI) system to assist in the creation and maintenance of process build instructions for our vehicle assembly plants. This system, known as the Direct Labor Management System, utilizes a restricted subset of English called Standard Language as a tool for the writing of process build instructions for the North American plants. The expansion of DLMS beyond North America as part of the Global Study Process Allocation System (GSPAS) required us to develop a method to translate these build instructions from English to other languages. This Machine Translation process, developed in conjunction with SYSTRAN, has allowed us to develop a system to automatically translate vehicle assembly build instructions for our plants in Europe and South America.