This paper presents a language vendor's perspective on the actual implementation of machine translation solutions in the translation/localization process. This lecture will be delivered at AMTA-2010 Conference, and a short video will accompany lecturer's speech.

This document describes the use of MT at GLTaC and provides an approach to determining if offering MT services is right for you. There is no single answer or approach to providing MT services so this is just one way an LSP has chosen to provide MT services.

This paper aims to give an insight into some of the challenges and opportunities from implementing machine translation in an enterprise environment. This is written from a business perspective rather than a technical one and highlights how Applied Language Solutions has designed and rolled out a series of customer specific machine translation solutions within our Enterprise.

PangeaMT is presented from our standpoint as a LSP keen to develop and implement a cost-effective translation automation strategy that is also in line with our full commitment to open standards. Moses lies at the very core of PangeaMT but we have built several pre-/post-processing modules around it, from word reordering to inline mark-up parser to TMX/XLIFF filters. These represent interesting breakthroughs in real-world, customized SMT applications.

Over the last two years, Adobe Systems has incorporated Machine Translation with post-editing into the localization workflow. Currently, the number of products using MT for localization has grown to over a dozen, and the number of languages covered is now five. Adobe is continuing to expand the number of products which use MT for localization, and is also looking beyond localization to other applications of MT technology. In this paper, we discuss some of our further use cases, and the varying requirements each use case has for quality, customization, cost, and other factors. Based on those varying requirements, we consider a range of MT solutions beyond our current model of licensed, customized commercial engines.

Avaya identified machine translation and post-editing as the next step in their strategy for global information management to deliver against the ever-present business objectives of “Increased Efficiency and Additional Localized Content”. Avaya shares how they assessed the market and selected their chosen vendor.

Although still in a nascent state as a professional translation tool, customized SMT engines already have multiple applications, each of which require clear definitions about quality and productivity. Three engine-training scenarios have emerged which are representative of real-world applications for the development and use of a customized SMT engines based on the availability of data. In the case that limited or no bilingual training data is available, a unique development process can be used to harvest and translate n-grams directly. Using this approach Asia Online and Moravia IT have successfully customized SMT engines for use in various domains. A partnership between an MT engine provider and a qualified LSP is essential to deliver quality results using this approach.

With pressure to offer content in many languages, many companies are considering machine translation for faster delivery and lower translation costs, yet MT is notorious for poor quality translation. How can you improve your content quality to make MT work for you? High quality source content eliminates many of the common roadblocks for using machine translation effectively. In this presentation, Jennifer Beaupre, Marketing Director and Kent Taylor, GM, acrolinx, will review what best practices have taught us about these topics: 1 Why is source content important when using machine translation? 2 How does source content affect translation costs? 3 How can source content improve the quality of MT output?

This paper discusses how to measure the impact of online content localized by machine translation in meeting the business need of commercial users, i.e., reducing the volume of telephone calls to the Call Center (call deflection). We address various design, conceptual and practical issues encountered in proving the value of machine translation and conclude that the approach that will give the best result is one that reconciles end-user (human evaluation) feedback with web and Call Center data.

This paper outlines the methodologies Microsoft has deployed for seamless integration of human translation into the translation workflow, and describes a variety of methods to gather and collect human translation data. Increased amounts of parallel training data help to enhance the translation quality of the statistical MT system in use at Microsoft. The presentation covers the theory, the technical methodology as well as the experiences Microsoft has with the implementation, and practical use of such a system. Included is a discussion of the factors influencing the translation quality of a statistical MT system, a short description of the feedback collection mechanism in use at Microsoft, and the metrics it observed on its MT deployments.

CA’s globalization team has a long term goal of reaching fully loaded costs of 10 cents per word. Fully loaded costs include the costs incurred for translation, localization QA, engineering, project management, and overall management. While translation budgets are gradually decreasing and volumes increasing, machine translation becomes an alternative source to produce more with less. This paper describes how CA Technologies tries to accomplish this long term goal with the deployment of MT systems to increase productivity with less cost, in a relatively short time.

The open source statistical machine translation toolkit Moses has recently drawn a lot of attention in the localization industry. Companies see the chance to use Moses to leverage their existing translation assets and integrate MT into their localization processes. Due to the academic origins of Moses there are some obstacles to overcome when using it in an industry setting. In this paper we discuss what these obstacles are and how they are addressed by the newly established Moses for Localization open source project. We describe the different components of the project and the benefits a company can gain from using this open source project.

In this presentation, we focus on integrating machine translation (MT) into an existing corporate localization and translation workflow. This MT extended workflow includes a customized post-editing sub-workflow together with crowdsourced, incentives based translation evaluation feedback routines that enable automated learning processes. The core of the implementation is a semantic repository that comprises the necessary information artifacts and links to language resources to organize, manage and monitor the different human and machine roles, tasks, and the entire lifecylce of the localization and translation supply chain(s).

PLuTO – Patent Language Translation Online – is a partially EU-funded commercialization project which specializes in the automatic retrieval and translation of patent documents. At the core of the PLuTO framework is a machine translation (MT) engine through which web-based translation services are offered. The fully integrated PLuTO architecture includes a translation engine coupling MT with translation memories (TM), and a patent search and retrieval engine. In this paper, we first describe the motivating factors behind the provision of such a service. Following this, we give an overview of the PLuTO framework as a whole, with particular emphasis on the MT components, and provide a real world use case scenario in which PLuTO MT services are ex- ploited.

This paper highlights the results and trends on post-editing and machine translation from the recent AMTA and SDL Automated Translation Survey. Then Continental Airlines and SDL share their experiences, and the benefits and challenges of human post-editing.

This paper describes PROMT system deployment at PayPal including: PayPal localization process challenges and requirements to a machine translation solution; Technical specifications of PROMT Translation Server Developer Edition; Linguistic customization performed by PROMT team for PayPal; Engineering Customization performed by PROMT team for PayPal; Additional customized development performed by PROMT team on behalf of PayPal; PROMT engine and PayPal productivity gains and cost savings.