Professor Jean-Jacques J Chanaron
email: jean-jacques.chanaron@wanadoo.fr

Life Cycle Assessment Practices: Benchmarking Selected European Automobile Manufacturers

Keywords: Life cycle assessment, automobile, best practices

With the rise of environmental concerns in the general public, re-appropriated by influential politicians, life cycle assessment (LCA) has become a widely used set of tools for the management of all impacts on environment by industrial products. LCA is carried out at the very early stages of product research, development and design. This is particularly true in the automobile industry where vehicle manufacturers (OEMs) are launching several new or revamped models each year. The automobile industry is therefore a very emblematic sector for best practices of LCA.

The paper is based on available literature and interviews with top LCA professionals in Germany-based OEMs.

Introduction

In recent years, life cycle assessment (LCA) has become a widely accepted and disseminated management tool in most large global corporations. This article is about benchmarking current if not best practices in LCA within a sample of European automobile manufacturers (OEMs), namely DaimlerChrysler, Ford and Volkswagen. In some occasions, references are be made to experiences by some of their subsidiaries (Jaguar and Volvo for Ford Group) and leading first tier suppliers.

The study has been carried out in 2004 and is based on an extensive web-based literature review1 and a limited number of open interviews with LCA professionals at high level within each company as well as academic specialists and general experts of LCA. The interviews were structured on a pre-defined set of questions derived from the key issues identified in the existing literature. The article is not a scientific paper per se but is presenting a synthesis and an analysis of the literature and interviews. The survey has been deliberately limited to European corporations.

1. Literature Background: Mapping the Context

Definitions

It is important to define precisely what life cycle assessment is since product and process life cycle has been considered as one of the key targets for several managerial disciplines and functions, such as general management (life cycle management), evaluation and control (LCA), design, as well as covers different functions.

Life Cycle Assessment is an analytical tool to systematically evaluate the environmental consequences of a product or activity holistically, across its entire life. LCA provides an adequate instrument for environmental decision support. Most companies have adopted the International Organization of Standards (ISO) Life Cycle Assessment guidelines defined in 14040 series documents published in 2002. Typically, energy and raw material requirements, atmospheric emissions, waterborne emissions, solid wastes, and other releases are mapped and inventoried over the entire life cycle of a product, package, process, material, or activity as shown in Figure 1. The impacts associated with these flows are evaluated. LCA can be conducted on product systems of varying complexity from milk and juice packaging to automotive transmission parts to larger more complex systems such as total vehicle and residential homes. Life Cycle Management is an analytical tool for managing the total life cycle of processes, products and services towards more sustainable consumption and production patterns. LCM integrates economic, social and environmental aspects into an institutional context and is applicable for organisations demanding a system-oriented platform for implementation of a preventive and sustainability driven management approach.

Life Cycle Design is a framework (figure 2) for integrating environmental considerations into product development by considering all stages of a product's life cycle, from raw materials acquisition through manufacturing and use to final disposal of wastes. Activities include identifying system requirements, selecting strategies for meeting these requirements, and evaluating tradeoffs among system alternatives. Successful environmental integration often must be achieved within the context of shortening time to market cycles, more stringent regulations, and global competitiveness. The objective of life cycle design is to enhance environmental performance across the life cycle while also optimizing functional performance, cost, and regulatory/policy requirements that influence the product system. Design analysis of these product systems highlights opportunities for improvement. Source: [START_REF] Spitzley | Life Cycle Design of Air Intake Manifolds[END_REF].

Life Cycle Costing (LCC) is a tool (figure 3) for evaluating all monetary costs associated with a system from acquisition, operation, maintenance, service and retirement. LCC addresses liabilities and hidden/less-tangible costs as well as externalities not accounted for in the current market system. As pointed out by [START_REF] Rebitzer | Life Cycle Costing in LCM: Ambitions, Opportunities, and Limitations. Discussing a Framework[END_REF], LCC in a part, obviously essential, of the overall assessment exercise. According to [START_REF] Contadini | Life Cycle Assessment of Fuel Cell Vehicles, A Methodology Example of Input Data Treatment for Future Technologies, LCA Methodology[END_REF], expert network activity is supposed to produce better information than individual opinion and any foresight analysis should be conducted using intensively the Delphi procedure. In developing a LCA of bio-ethanol, [START_REF] Riley | Bio-ethanol from Corn Stover: A Life Cycle Assessment[END_REF], from the National Renewable Energy Laboratory, within the U.S. Department of Energy, emphasized the key role of all stakeholders in the LCA process (figure 5). • LCA is only one tool within the life cycle philosophy and the sustainable development way of thinking which is itself vaguely if not badly defined and at least subject of deep debates; • LCA is targeting the selection of the "best" route between a departure point A and a destination B which is supposed to be better but is in reality full of uncertainties; • LCA is not a universal inventory and assessment exercise which enables a multitude of decisions, each one being aligned to a specific decision context; • LCA has no universal interpretation since much additional information might be expressed. Other comments that could be found in the literature:

• LCA data, when available, are challengeable in quality, reliability and Replicability as well as scientific validity;

• LCA should be developed within a more general framework including social impacts as well as environmental. • We strive to develop products which in their respective market segments are highly environmentally responsible. Our approach to environmentally acceptable design covers the entire product spectrum of the DaimlerChrysler Group, taking into account the product life cycle from design through disposal or recycling. Continuously improving the environmental performance of our products is one of our important goals. DaimlerChrysler is committed to the ongoing pursuit of this objective, especially in its research and development activities".

• "We plan all stages of manufacturing to provide optimal environmental protection. DaimlerChrysler sees itself as a leader in the ongoing development of environmentally responsible production technology which minimizes the burden on the environment. This includes proactive behavior to prevent or minimize the impact of accidents which may adversely affect the environment. Particular emphasis is given to the application and continuing development of technologies which save energy and water, and which are characterized by minimal emission and waste levels. This includes the development of effective environmental assessments, emission controls, reuse, and recycling strategies. DaimlerChrysler aims to achieve closed-loop material cycles. Our ultimate goal is waste-free production. DaimlerChrysler requires its suppliers and contractual partners to comply with all applicable laws and regulations and encourages them to pursue proactive environmentally responsible practices. Contractors working on DaimlerChrysler properties also must comply with the location's own standards and requirements."

Environmental protection is one of the fundamental corporate objectives of the DaimlerChrysler Group. In this context, environmental protection is an integral component of the corporate strategy, designed to ensure long-term value creation. DaimlerChrysler's goal of maximum product quality includes compliance with stringent environmental standards and careful treatment of the natural foundations of life. Accordingly, the approach to environmentally acceptable product design requires careful consideration of the entire product life cycle from design, production and use to disposal or recycling.

According to DaimlerChrysler, the environmentally compatible design of a vehicle should begin long before the first prototype takes shape on a CAD screen. In the DaimlerChrysler laboratories, environmental protection is built into the company's products from the outset, so to speak -in passenger cars, buses, vans and trucks alike. As the ecological impact of a vehicle is largely determined during the initial stages of its development, the earlier it is started, the more it could be achieved in terms of environmental protection, and the lower the cost and effort supported by the corporation will be (Figure 6): Even minor measures taken at the very early development stage would have significant effects at later stages and yield tangible reductions in environmental effects as well as in costs. By contrast, it is extremely difficult and costly to modify components at an advanced stage and even worse at production stage. An illuminating example is the extra cost impact of modifying the Mercedes Class A and the Smart after failure at the élan stability test some years ago.

This explains why a team of experts at the Design for Environment (DfE) department spend their time designing integral concepts for vehicles. Design for Environment deals with the selection of suitable raw materials and substances as well as with recycling-friendly design and production, enabling subsequent reuse or recycling of end-of-life vehicles and responsible disposal of replaced parts. Consequently, the DfE team includes experts from various areas: life cycle assessment; disassembly and recycling planning; materials and process engineering; design; and production. The team's activities are seamlessly integrated into each step of the development process in a "simultaneous engineering" approach.

Only an assessment of all environmental impacts across the vehicle's entire lifetime will reveal its overall energy consumption, waste generation and emission levels. Such an analysis covers all elements of the product life cycle, from raw materials extraction, through material manufacture, production and utilization, to disposal. As shown in Figure 7, a vehicle life cycle assessment (LCA) enables the Design for Environment experts to record and assess each component. The findings for the Mercedes E Class are the following: For materials, DaimlerChrysler claims that effective disassembly and recycling concepts as well as new technologies ensure fewer and fewer disposal of replaced parts from passenger cars and commercial vehicles. Using the MeRSy recycling management system, 30,000 metric tons of materials are now returned to the recyclable material loop each year in Germany, Austria, Switzerland and the Benelux countries alone. The system has been recently extended to Smart, Chrysler and Jeep vehicles.

Environmental management and organization

The Environmental Protection Guidelines have been approved by the Board of Management. They define the environmental policy of the DaimlerChrysler Group and describe the commitment to integrated environmental protection that addresses environmental impacts at their roots, assesses in advance the ecological implications of production processes and products, and takes these findings into account in corporate decision-making. Appropriate control and monitoring procedures and measures have been implemented.

Responsibility for environmental protection at DaimlerChrysler lies with the Group's Chief Environmental Officer, Prof. Herbert Kohler, who reports to the Board of Management on these matters at regular intervals, including a verifiable environmental report, published annually, which is documenting the Group's activities and achievements. His key tasks include ensuring that our environmental management system functions effectively. The efficiency of the system is regularly validated worldwide by external audits. 91% of DaimlerChrysler employees currently work at plants with certified environmental management systems. DaimlerChrysler is also increasingly focusing on environmental requirements in its suppliers.

At present, the main focus is on integrating the management systems for quality, environmental protection and industrial health and safety. This will enable DC to integrate environmental protection tasks more fully into the core functions and processes of the respective departments instead of dealing with them as separate processes. Implementation of the integrated concept was decided on in 2002, initially for the Mercedes-Benz passenger car plants in Germany and the USA. At the Chrysler Group plants, the environmental management system was integrated when it was introduced into the existing quality assurance system (Manufacturing Quality Assurance System, MQAS).

Ecological site audits are another important topic. These help DaimlerChrysler determine environmental risks, reduce them and raise all of our sites to a high environmental standard.

To this end, a procedure has been developed that has already been successfully applied at numerous production and sales sites in recent years. In 2002, the group took a further step towards the goal of implementing it worldwide.

Other central elements of product-and production-related environmental protection at DaimlerChrysler are environmental education and communications. Unless the employees are aware of the environmental issues and committed to resolving them, DC cannot achieve the continuous improvements in environmental protection that has been planned.

Responsibility for the implementation of and adherence to environmental protection measures has been assigned to specific employees in all functional areas, from development and production to sales and service, and at all corporate staffs.

The Environmental Protection Guidelines are binding for all the Group's employees and at all corporate locations. Accordingly, the Group supports and encourages all employees to put environmental protection into practice at the workplace at his or her own initiative. Measures implemented at the various corporate locations are regularly assessed and subject to a process of continual improvement. In order to comply with its self-imposed environmental protection standards, the DaimlerChrysler Group draws up its own environmental goals. The ecological programs required to meet these goals are monitored through a comprehensive auditing process aimed at measuring compliance with procedures and regulations, and when necessary, corrective actions are taken to improve performance.

An extensive catalogue of environmental targets defines the environmental protection roadmap at DaimlerChrysler. These targets are up-dated annually and a review of the extent to which they have been achieved is carried out.

Data Collection

A systematic compilation of key environmental data from the DaimlerChrysler German plants in 1992. In 1997 and 1998, data acquisition was gradually extended to include production plants outside Germany in which the DaimlerChrysler Group is the majority shareholder.

The data for resources (input) and waste and emissions (output) is restricted to DaimlerChrysler's own locations. The specific values arrived at by these means are approximate guidelines, because they take no account of different depths of vertical manufacturing, the frequently substantial differences in the products built by the various divisions or the peculiarities of the different integrated production networks.

2.1.4. Other Studies [START_REF] Sorensen | Total Life Cycle Assessment of PEM Fuel Cell Car[END_REF] has studied PEM fuel cell cars of which the DaimlerChrysler f-cell (35MPa H2 fuel PEMFC/electric motor) comparing with a Toyota Camry powered by an Otto engine and a VW Lupo powered by a common rail diesel engine.

Comments

DaimlerChrysler is basically following the model developed by the VDA which is clearly considered so far as the standard for products designed for the European market.

It is important to pinpoint that even if they are very well identified and occasionally involved in international scientific conferences, DaimlerChrysler experts for life cycle assessment still argue that information about life cycle assessment practices are very confidential since they might enlighten the corporation product development strategy and the methods in place, then opening too much to competitors or potential critical views by environmental organizations. It is indeed particularly true in Germany and by extension in Europe with the rise of environmental concerns with customers and governments, including in emerging countries such as China where the corporation is industrially present.

Ford Motor

Ford is currently using life cycle assessment and life cycle design for most of its new vehicles (passenger cars as well as trucks), for its research vehicles (hybrid, electric, etc.) and for most of its mechanical components. Ford Europe started to use life cycle assessment for the first time in 1992. Early developments were made in the United States. So far more than 100 life cycle assessment studies have been carried since then.

European Subsidiaries

Jaguar

Ford's subsidiary Jaguar has used life cycle assessment for the development of the new X-Type Jaguar. In 1998, Jaguar was assessed by the Vehicle Certification Agency (VCA). This resulted in Jaguar Cars being awarded certification to ISO 14001, the International Standards Organisation's accreditation for environmental management systems.

Jaguar's commitment to the environment starts at the top with Chairman Nick Scheele who stated that environmental performance as a strategic issue. Jaguar's first formal recognition that environmental issues were a priority came in 1992 with the formation of an Environmental Strategy Committee, set up primarily to look at ways of making its cars more 'eco-friendly'. The Committee's ideas were rapidly translated into actions as the company introduced a series of component and materials changes with long-term environmental benefits.

Having successfully raised the environmental credentials of the products themselves, Jaguar's environmental team also turned its focus to the company's processes. Dr. Geraint Williams spearheads Jaguar's ISO 14001 task force:

According to Joe Greenwell, Chairman and Chief Executive Officer, Jaguar's Sustainable Development Policy details its long-term aims and commitments. This is supported by a strategy framework and a series of Key Sustainable Development Goals, which set direction and clear targets for the company. Jaguar is currently developing a 10-year vision to guide its sustainable development route map for the future and to ensure that this is satisfying their stakeholders´ requirements.

An essential element to the success of Jaguar's policies and strategies is ensuring their integration into the decision-making process. Sustainable development has been incorporated within Jaguar's high-level Business Plan Scorecard. These requirements translate down to all the functions and progress is monitored regularly by senior management.

Volvo

The Volvo branch, whose brand image is very much linked to safety and ecology, is indeed very much engaged in life cycle assessment.

Before being integrated within Ford Group, Volvo has developed in 1990 a specific approach named Environmental Priority Assessment (EPS) (Figure 8) as part of its strategic choice made official in 1989 towards clean and safe vehicles. The customer general environmental view is evaluated by means of the so-called willingness to pay for changes (WTP) for emissions and WTP for alternative renewable methods for raw material resources. EPS has been used for the Volvo S80-model.

Since then Volvo has developed SPINE (Sustainable Product Information Network for the Environment), a database for LCA with activity as main concept, e.g. raw material extraction, assembly, use of a product, etc., with inputs and outputs described by flows connecting activities. Software handles the data and the structure, i.e. the whole modelling of the set of activities.

Since 2000, the process of environmental decisions within the product Development Department is the following (Figure 9): Volvo is making use of E-FMEA, a group method for identifying important environmental aspects early in the product development phase. During meetings, a group of engineers combines skills and know-how and experience to identify every conceivable environmental aspect which a product or a component may have during its lifetime.

Practical Applications of LCA at Ford Motor Full vehicle LCA

A very limited number of life cycle assessment studies have been published concerning Ford vehicles of the current model range.

Innovations and Vehicles for the Future

The University of Carnegie-Mellon is working closely with Ford. The University of Carnegie-Mellon ( 2003) suggests there are a variety of methods to be used for life-cycle assessment. They suggest the following matrix (Figure 10) crossing life cycle stages with life-cycle impacts: Volvo used its EPS methodology for evaluating the environmental impact of its ECT Environmental Concept Truck (ECT) prototype, a hybrid vehicle using a gas turbine for charging nickel-metal hybrid battery pack [START_REF] Karlsson | EPS as a Life Cycle oriented System Assessment Tool to Facilitate Industrial Learning about Relations to the Environment[END_REF].

Components

For many components, Ford is teaming with the Centre for Sustainable Systems from the University of Michigan, Ann Arbor:

In 1999, the partnership analysed the lower plenum of the air intake manifold for use with the 5.3 L F-250 truck engine. The life cycle design (LCD) methodology has been applied to three alternatives: a sand cast aluminium, a lost core molded nylon composite and a vibration welded nylon composite.

The methodology is well known 1. Chart the process flow from production of raw materials to shredding, recycling and waste disposal, including manufacturing and use; 2. Collect data for all stages including performance analysis and cost analysis; 3. Evaluate environmental inventory; 4. Evaluate costs; 5. Assess internal and external environmental requirements; 6. Evaluate weaknesses and bias in data collection; 7. Make Decision.

It is important to note (see Table 3) than Ford-CSS are using both internal and external requirements for environment protection in their decision making process: Then, using the manifold example, the decision might be made using several criteria: [START_REF] Sullivan | Sustainability metrics for vehicles[END_REF], from Ford Research Laboratory in Dearborn (USA), is advocating life cycle assessment and the development of sustainability or eco-efficiency metrics for vehicles, i.e. measures of resources and materials consumed per unit output of products couples with measures of environmental performance as well as social-equity of such products.

According to [START_REF] Louis | Integration of Environment within the Product Process Development at Volvo Car Corporation[END_REF], the Volvo LCA-EPS has been and is still extensively used for components when different alternatives are available such as the choice between materials, e.g. the tailgate panel structure (5 th door). As indicated in Figure 11, the methodology is the following2 : According to a Technical Specialist Vehicle Recycling at Ford Werke AG, Vehicle Recycling, Ford is a major partner in two research projects, the first one dealing with life cycle assessment methodology, the second with bio-fuels and materials:

• Enhancing the Application Efficiency of Life Cycle Assessments for industrial applications with Motorola and Alcan. This research project focuses on the implementation of LCA in industry and aims to enhance the application efficiency by: 1. Improving the LCA methodology in regards to focusing on the most significant processes, specifically concentrating on the life cycle inventory (LCI), and 2. The use of life cycle inventory data for life cycle costing assessments. Ford is extensively using ISO 14000 standards since these norms have been designed, discussed and adopted by all ISO members states and organisations. But Ford LCA experts have also elaborated complementary assessment such as stakeholders' needs and priorities. Life cycle assessment is a key strategic tool to foresee and understand the futures issues at stake during the life cycle as a whole. It is also a benchmarking exercise allowing comparison with competition and with older models and previous technological choices. Ford has developed its own internal life cycle assessment software tool which is used for most life cycle assessment studies that are carried out and often published since the head of the team is a member of the European automotive life cycle assessment group and very active in the life cycle assessment community.

Life assessment studies are carried out at three different levels:

• Environmental department;

• Product development, in particular people in charge of vehicle integration; • Research when future vehicle models are concerned.

Each life cycle assessment is carried out by a multi-functional team composed with representatives of three departments or functions: environment, research and vehicle integration.

Volkswagen Group

Similarly to its main European competitors, Volkswagen Group is also making an extensive use of life cycle assessment for its new models and new components. The advertising of ISO 14000 labels for the company's flagship, the Golf, is indeed very important, in particular in Germany where the environmentalists are quite influential. Figure 12 is presenting the VW process. all stages of the life cycle, including assessing the environmental impacts in its suppliers' plants.

It is important to pinpoint that Volkswagen Group claims that LCA can only be carried out with the support of independent experts when delivering the ISO 14040-41 standards.

A good example of recent assessment exercise is the Golf A4 (Figure 13 & Table 5) life cycle assessment (Schweimer & Levin, 2001), where comparisons could be made on several generations of the same model: Volkswagen is one of the four OEMs to be involved in the FUERO (www.fuero.org) project together with Renault, Peugeot-Citroën and Volvo about fuel cells components with a 4.5 million euro budget. The whole work package 8 is dedicated to the life cycle assessment. [START_REF] Nicolay | A Simplified LCA for Automobile Sector, Comparison of ICE (petrol and diesel), electric and hybrid vehicles[END_REF] compared the LCA results of a Seat Ibiza TDI with Mitsubishi 2000 cm 3 petrol, an electric Peugeot 106 and a Toyota Prius.

Like DaimlerChrysler, VW LCA experts still consider information about organizational issues and methodologies for LCA as strictly confidential.

Conclusion(s)

The survey leads to several interesting conclusions:

1. 4. As far as method is concerned European OEMs are applying ISO 14000 standards, but they have also elaborated complementary assessment techniques in order to integrate other industry-specific and often confidential information. Therefore, most of them are reluctant to publish full reports. They rather prepare a specific and short document which is made public some time after the commercialization of a new vehicle.

5. European OEMs use the following software tools: GaBi, which is by far the most widely used in the European automobile industry, SimaPro and Umberto. Some interfaces exist between LCA and broader management tools, but a full integration is probably not planned and may be also not the right direction.

6. All OEMs are using LCA for vehicle (new models), platforms and components as well as for assessing innovative technologies and vehicles of the future.

7. By far, the most important problem that European OEMs are facing with LCA is the scientific validity of the measurement technique. Then the availability and quality of data are considered as challengeable. It is important to keep in mind that life cycle assessment was developed thirty years ago [START_REF] Gauthier | Sustainable Development: a New Life Cycle Assessment[END_REF], in order to respond to the specific needs of companies seeking to respect the environment while developing and improving their products. It is not more than a decisionmaking tool, the use of which being governed by an international standards 14040, 14041, 14042 and 14043. As pointed out by [START_REF] Contadini | Results of a Life Cycle Assessment Using Uncertainty Analysis of Fuel Cell Vehicles Within the South Coast Air Basin of California in 2010[END_REF], life cycle assessment involves some subjectivity and uncertainty, especially with new technologies and future scenarios.

There is still research to be carried out in the near future about practices within other OEMs, in particular in the United States, Korea and Japan. A survey of suppliers might provide very interesting information about the dissemination of LCA practices within the automotive value chain, i.e. the automotive system as defined by de Banville, [START_REF] Chanaron | Vers un système automobile européen[END_REF]. And obviously, practices in this specific sector must be benchmarked with best practices in other industries. It would also be very interesting to compare practices for traditional technologies and for emerging technologies such as hybrid and fuel-cell vehicles.

Figure 1 .

 1 Figure 1. General Product Life Cycle

Figure 2 .

 2 Figure 2. Product Life Cycle System

Figure 3 .

 3 Figure 3. Life Cycle Costing

Figure 4 .

 4 Figure 4. Future Technologies LCC

Figure 6 .

 6 Figure 6. Influence and Effects of Design for Environment (DfE) at DC

Figure 7 .

 7 Figure 7. Environmental Impact on the lifecycle stages of a passenger car using the example of the Mercedes Benz E-Class

Figure 8 .

 8 Figure 8. Volvo's EPS Model

Figure 9 .

 9 Figure 9. Volvo Decision Process

Figure 10 .

 10 Figure 10. Carnegie Mellon LCA Matrix

Figure 11 .

 11 Figure 11. The EPS Methodology

Figure 12 .

 12 Figure 12. VW LCA Process

Figure 13 .

 13 Figure 13. Golf 4 LCA Model

Table 1 .

 1 Examples of LCA UsersA benchmarking survey carried out in 1999 (Table2) has shown that 92% of the 14 large corporations involved in motor vehicle industry were then carrying LCA and Design for Environment studies:

		Figure 5. Stakeholders' Approach
	Stakeholder Input			Stakeholder Input
	Goal and Scope	Collect Data	Construct Model	Draft Result	Modify/Update Model
					Final Report
	Source: Riley & Sheehan, J., (2000).
	1.2. LCA software
	• EIOLCA -Economic Input-Output LCA at Carnegie Mellon University • GaBi 3 -(Ganzheitliche Bilanzierung) -University of Stuttgart (IKP)/PE Product Engineering • IDEMAT -Delft University Clean Technology Institute Interduct Environmental Product Development • KCL-ECO 3.0 -KCL LCA software • LCAiT -CIT EkoLogik (Chalmers Industriteknik) • LCNetBase -Life cycle assessment using traceable US data -Sylvatica • SimaPro 5.0 for Windows -PRé Consultants • SPOLD -Society for the Promotion of Life-cycle Assessment Development • TEAM(TM) (Tools for Environmental Analysis and Management) -Ecobalance, Inc. • Umberto -An advanced software tool for Life Cycle Assessment -Institut für Umweltinformatik
	1.3. Users of LCA in the automotive industry
	According to the Life Cycle Initiative launched in 2002 by the United Nations Environment Program (UNEP) and the Society of Environmental Toxicology and Chemistry (SETAC), the life cycle assessment approach is used by the following corporations (Table 1):

Obviously, there is a lot of software available on the market place. In 2000, IVL conducted a survey on 24 systems on behalf of the Swedish Industrial Research Institutes. In 2003, the most disseminated LCA software were the following:

• Boustead Consulting Database and Software • ECO-it: Eco-Indicator Tool for environmentally friendly design -PRé Consultants • EcoPro -sinum Corporate Environmental Management • EDIP -Environmental design of industrial products -Danish EPA

Table 2 .

 2 LCA and DES in Selected Industries

	Source: UNEP-SETAC, (2000).

1.4.

Limitations to LCA practices Many comments and critics have been posted on life cycle assessment methodology as standardized by ISO 10040. According to Bauer (2002):

Table 3 .

 3 Internal and External Environmental Requirements for Ford Motor

Source: Spitzley, Keoleian, (1999),

Table 4 .

 4 Ford's Criteria for the Truck Air Manifold

  Ford emphasises two reasons for engaging in LCA: this has been a deliberate choice at board level which did proclaim that Ford cars should have the minimum environmental impact and the pressure from the European institutions is playing a key role in such option. It is interesting to note that Ford is not considering economic factors such as financial and tax incentives and marketing to customers as influential so far. For Ford, environment issues are not a selling weapon in Europe since they do not yet appeal to customers.

	• Review of Life Cycle Assessment studies for Biofuels, Biomolecules and Biomaterials with funding by Environmental and Energy Management Agency France (ADEME) and in partnership with Bonnard & Gardel with a case study of a Front Sub-frame System;
	2.2.3. Comments

Table 5 .

 5 Comparing various Golf and Lupo LCA results

  The main reason for European OEM for engaging in life cycle assessment is linked to political pressures from different levels. Indeed the European institutions are by far the strongest and most influential lobby. Environmental organizations play a secondary role everywhere but in Germany: therefore German vehicle manufacturers have to be active and communicate on such issues.2. Economic factorshave no real influence. The commercial or marketing factors have a very limited impact: customers are not yet ready to pay a visible premium for a "clean" and fully recyclable car. Environmental issues are a weak selling argument in Europe. 3. On the other hand, strategic factors might play a crucial role. Engaging in life cycle assessment is a board-level decision and the professional organizations at national level, VDO in Germany and ACEA at European level, are actively involved in disseminating methods and best practices in life cycle assessment.

Table 6 .

 6 Main problems with LCA data reliability 1 They consider that some assessment measures are not scientifically valid 2 Some data are simply not available 3 Poor quality of data 4 Comparability over time is not feasible 5 External suppliers of data do or could not reply 5 Methodological bottlenecks: new methods and tools should be developed 8. Generally speaking, LCA studies are carried out at corporate division level. But sometimes they are also embedded in line management. Most LCA assessment experts are part of a Design for Environment and/or recycling department. Such organization is explicitly multi-functional.

All interesting articles and books have been included in the reference list even when not explicitly quoted in the text itself.

ELU is the equivalent of Euro.

In German, sachbilanz.


Raphaël Rubino

Stéphane Huet

Fabrice Lefèvre

Georges Linarès

Statistical Post-Editing of Machine Translation for Domain Adaptation

This paper presents a statistical approach to adapt out-of-domain machine translation systems to the medical domain through an unsupervised post-editing step. A statistical post-editing model is built on statistical machine translation (SMT) outputs aligned with their translation references. Evaluations carried out to translate medical texts from French to English show that an out-of-domain machine translation system can be adapted a posteriori to a specific domain. Two SMT systems are studied: a state-of-the-art phrasebased implementation and an online publicly available system. Our experiments also indicate that selecting sentences for post-editing leads to significant improvements of translation quality and that more gains are still possible with respect to an oracle measure.

Introduction

Phrase-Based Machine Translation (PBMT) is a popular approach to Statistical Machine Translation (SMT) that leads to accurate translation results [START_REF] Zens | Phrase-based statistical machine translation[END_REF][START_REF] Marcu | A phrase-based, joint probability model for statistical machine translation[END_REF][START_REF] Koehn | Statistical phrase-based translation[END_REF]. The statistical models used in PBMT are based on the probabilities of bidirectional alignment of phrases between two sentences in the translation relation. The linguistic resources used to estimate such probabilities are parallel corpora and the main resulting statistical model is a translation table. Therefore, parallel corpora are the cornerstone for high quality translation. However, such resources are expensive to construct. This lack of parallel data still remains an issue in PBMT. This phenomenon is accentuated by the diversity of texts to translate, in terms of origin and domain. As explained in [START_REF] Sager | English special languages: Principles and practice in science and technology[END_REF], most of human activities involve a specific language or a subject language. A specific domain can be characterized by particular terminology or syntactic and discourse structures. As building domain specific translation systems for each domain is unreasonable, we assume that domain adaptation of out-of-domain translation systems can be one of the solutions to address the diversity of specific domains.

Although current machine translation systems can lead to impressive accuracy, translated texts require sometimes human post-processing to be usable. However, editing a posteriori can be costly depending on the amount of corrections required by machine translation outputs. Therefore, the automation of post-editing is an important task which can lead to higher quality machine translation without requiring human intervention.

In this paper, we propose a statistical postediting (SPE) approach to adapt SMT systems to specific domains. We focus on translating texts in the medical domain from French to English. Several SMT systems are studied and we propose different methods to include the in-domain data into the translation process. We evaluate how translation quality can be improved with a post-editing step based on a phrase-based alignment approach. Two sets of experiments are presented in this paper: one applying SPE consistently on all the sentences and one resorting to SPE only on selected sentences.

The remainder of this paper is organized as follows. Section 2 presents the phrase-based postediting approach. In Section 3, we propose an ex-perimental setup and give details about the data, the language models, the translation and the postediting systems used in our experiments. Section 4 evaluates each SMT system on a domain specific translation task, then Section 5 analyses the effect of a standard post-editing system on translated texts. Section 6 presents our approach to select sentences for post-editing. Finally, Section 7 concludes this paper.

2 Phrase-Based Statistical Post-Editing

SPE Principles

The post-editing of a machine translation output consists of the generation of a text T from a translation hypothesis T of a source text S. When a PBMT system is built on bilingual parallel data, a phrase-based SPE system requires monolingual parallel texts. Recent approaches on SPE are based on three-part parallel corpora composed of a source language text, its translation by an MT system and this output manually post-edited [START_REF] Knight | Automated postediting of documents[END_REF][START_REF] Allen | Toward the development of a post editing module for raw machine translation output: A controlled language perspective[END_REF]. If SPE can correct mistakes made by machine translation systems, it can also be used to adapt machine translation outputs to specific domains.

SPE for Adaptation

The research presented in this paper addresses the issue of adapting an out-of-domain machine translation system using a small in-domain bilingual parallel corpus. We study various uses of out and in-domain data to build Language Models (LMs) and Translation Models (TMs) inside the sourceto-target language PBMT. Then, we evaluate the post-editing model using out and in-domain data to build LMs and in-domain data only for the SPE model. We also describe a new method to select sentences using classifiers built with the BLEU criterion [START_REF] Papineni | BLEU: A method for automatic evaluation of machine translation[END_REF].

Figure 1 illustrates the general architecture of our experimental setup, described in the next section. The source language part of the in-domain parallel corpus is first translated into the target language by an SMT system. Then, the generated translation hypotheses are aligned with their translation references in order to form a monolingual parallel corpus and to build a SPE model. When a test corpus is translated and has to be post-edited, we propose two different approaches. The first one is a naive application of SPE which post-edits all the sentences of the test corpus. The second one is based on a classification approach that aims to avoid a degradation of translation quality at the sentence level. For this last approach, we build a sentence classification model to predict whether or not the sentences from the test set can be improved with SPE.

Related Work

In (Simard et al., 2007a), the authors propose to post-edit translations from a Rule-Based Machine Translation (RBMT) system using the PBMT system PORTAGE [START_REF] Sadat | PORTAGE: A phrase-based machine translation system[END_REF]. A qualitative study of phrase-based SPE is presented by [START_REF] Dugast | Statistical post-editing on Systran's rule-based translation system[END_REF][START_REF] Dugast | Statistical post editing and dictionary extraction: Systran/Edinburgh submissions for ACL-WMT2009[END_REF], where the Systran system outputs are post-edited with PORTAGE and MOSES. The authors report gains up to 10% absolute of BLEU.

In [START_REF] Isabelle | Domain adaptation of MT systems through automatic postediting[END_REF]Simard et al., 2007b), it is shown that a generic, or out-of-domain, RBMT system can be adapted to a specific domain through phrase-based SPE. Domain specific data are introduced at the post-editing level, which globally improves the translation quality. Besides, de [START_REF] De Ilarraza | Statistical postediting: A valuable method in domain adaptation of RBMT systems for less-resourced languages[END_REF] propose the same architecture, phrase-based SPE following a RBMT system, and introduce a small amount of in-domain data to train the SPE model, as well as morphological information in both systems.

More recently, [START_REF] Béchara | Statistical post-editing for a statistical MT system[END_REF] design a full PBMT pipeline that includes a translation step and a post-editing step. The authors report a significant improvement of 2 BLEU points for a French to English translation task, using a novel contextaware approach. This method takes into account the source sentences during the post-editing process through a word-to-word alignment between the source words and the target words generated by the translation system. This latter work is, to the best of our knowledge, the first attempt to combine two PBMT systems, one for translating from the source to the target language, and another one for post-editing the first system output.

This kind of PBMT pipeline had already been suggested by previous authors [START_REF] Isabelle | Domain adaptation of MT systems through automatic postediting[END_REF][START_REF] Oflazer | Exploring different representational units in English-to-Turkish statistical machine translation[END_REF]. Let us note that their work is not targeting to improve the outputs of an out-of-domain SMT system with adaptation data as in our approach. Another recent approach related to our work was presented in [START_REF] Suzuki | Automatic post-editing based on SMT and its selective application by sentence-level automatic quality evaluation[END_REF] to select sentences for post-editing. The authors present an architecture

Experimental Setup

In brief, the general idea of the work presented in this paper is to increase the quality of in-domain translations, generated by an out-of-domain SMT system, through a post-editing step. In order to thoroughly evaluate our approach, two SMT systems are considered to translate from the source language to the target language: the MOSES PBMT implementation [START_REF] Koehn | Moses: Open source toolkit for statistical machine translation[END_REF] and the GOOGLE TRANSLATE online system 1 . The post-editing step is then performed using MOSES in both cases. The latter case (the online system) will help to justify our approach showing that a powerful yet fixed MT system can be profitably combined with a system trained on a small set of in-domain data. The approach is evaluated at two levels: first, we evaluate the accuracy of each translation system on a domain specific translation task. Second, we focus on the use of SPE systems to process each translation system output. Section 3.1 introduces the out and in-domain data used in our experiments. These data can be combined in different ways inside LMs and TMs; the resulting translation systems are described in Section 3.2. Then, Section 3.3 provides information about our SPE models.

Resources

Out-of-domain data are presented in Table 1. The bilingual parallel corpora are the sixth version of the Europarl corpus [START_REF] Koehn | Europarl: A parallel corpus for statistical machine translation[END_REF] and the United Nations corpus [START_REF] Rafalovitch | United Nations general assembly resolutions: A six-language parallel corpus[END_REF] The in-domain domain data used in our experiments are taken from the EMEA corpus (Tiedemann, 2009), made out of PDF documents from the European Medicines Agency 4 . The source documents are associated with three biomedical categories: general medical documents and public evaluation reports about human or veterinary treatments. This corpus is particularly interesting because it contains medical terminology and specific linguistic structures. Since the EMEA corpus contains lots of repeated expressions (on med-ical prescriptions for instance), we removed duplicates. Furthermore, short sentences of one word and long sentences exceeding 80 words were discarded. The resulting corpus is split separately for each category into three parts, which globally leads to three corpora: a 156k-sentence training set, a 2k-sentence development set and a 2k-sentence test set.

Initial SMT Systems

The online translation tool, noted com in the remainder of this paper, cannot be modified. It provides us with translation hypotheses which can be scored and post-edited in order to evaluate our approach. The MOSES PBMT implementation can be used to train a translation model from parallel corpora. Several PBMT systems are built, based on the bilingual and monolingual data used.

Three different 5-gram Kneser-Ney LMs are trained on the resources, using the SRILM toolkit [START_REF] Stolcke | SRILM-an extensible language modeling toolkit[END_REF]. A first one (LM g ) is built on the monolingual out-of-domain data while a second one (LM m ) is built on the target language part of the medical (in-domain) corpus. These two models are combined through a linear interpolation (LM g+m ). For this last LM, weights were computed from the perplexity optimization on the EMEA development corpus, and vocabulary was fixed to 1 million words taking all the words of the in-domain corpus and the most frequent words from the out-of-domain corpora. Let us note that a high weight of 0.9 is associated with the medical LM despite its small size, which is explained by the great specificity of the medical domain.

Three Translation Models (TMs) incorporating a phrase table and a lexicalized reordering model are also built using MOSES: one (T M g ) from the out-of-domain data, one (T M m ) from the medical set and a last one (T M g+m ) from all the parallel corpora. For that purpose, bilingual data are aligned at the word level using the IBM 4 model [START_REF] Och | A systematic comparison of various statistical alignment models[END_REF] with MGIZA++ [START_REF] Gao | Parallel implementations of word alignment tool[END_REF] . The score weights of a given TM and a selected LM are finally computed in each tested configuration using the Minimum Error Rate Training (MERT) method [START_REF] Och | Minimum error rate training in statistical machine translation[END_REF] to optimize BLEU on the EMEA development corpus. To mix the information from the out and in-domain in T M g+m , we resorted to the multiple translation tables option implemented into MOSES. With this feature, we can provide two translation tables to the decoder; the decoder first retrieves translation pairs from the in-domain phrase table, and resorts to the out-of-domain phrase-table as a fall-back.

SPE Systems

In order to build the SPE system for domain adaptation, we decide to translate the EMEA training corpus with each tested SMT system. Then, with the output of each system aligned with its translation reference, we build an SPE model using MOSES with default parameters. For the tuning process, we used the same in-domain development data as the SMT systems, this time with the SMT output aligned with its translation reference. Let us note that the weight optimization was repeated for each tested PBMT configuration.

Translating In-Domain Data

The first set of experiments deals with the translation of the domain specific, or in-domain, test corpus.

The results are given in terms of BLEU scores in Table 2 with several uses of the previously described TMs and LMs. Pairwise comparisons between systems is made using approximate randomization as implemented in the evaluation tool FASTMTEVAL [START_REF] Stroppa | A cluster-based representation for multi-system MT evaluation[END_REF]. These results indicate that the best configuration is T M g+m LM g+m , with a BLEU score of 47.3%. This score is not significantly higher (p-value=0.75) than the one obtained by T M g+m LM m with an in-domain language model. These observations show that the specificity of the medical domain, including terminology and syntactic structures, cannot be improved by the introduction of out-of-domain data into the LM. For the translation model, however, the combination of the two phrase tables is the best configuration in the presented system comparison. The same conclusion about the importance of in-domain data can be derived from the results obtained with T M g built on the sole out-of-domain data. A 10 points BLEU improvement is indeed obtained using LM m instead of LM g . Interpolating the two LMs introduces noise and decreases by 1 BLEU point the result obtained with LM m only. Finally, let us note that the online system GOOGLE TRANSLATE has a BLEU score only 1.5 points lower than a PBMT system built using small-sized but highly relevant data.

SMT

Post-Editing Translations

After the translation step, SMT outputs are postedited. Several SPE models are built from the translations of the EMEA training corpus generated by each SMT system. We decide to compute two scores: a first one for which all the sentences from the test corpus are post-edited, and a second one for which only sentences are post-edited if their sentence-level BLEU is improved (oracle). The computation of this oracle score relies on the reference translation and is done to estimate the potential of SPE.

Online System

The online translation tool already leads to good results in terms of BLEU score. The in-domain test corpus translated by the online system is postedited by its SPE system. The results are shown in Table 3. Computating p-values to compare results before and after SPE exhibits a significant difference (p = 0.001 for BLEU and p = 0.05 for the oracle score).

System

% BLEU (oracle) com 44.9 + SP E m LM m 46.8 (53.3) + SP E m LM g+m 47.9 (53.5) Table 3: BLEU scores of SPE on the online system output.

Two SPE systems are built with a different LM. With the medical LM (SP E m LM m ), the BLEU score of the post-edited translation reaches 46.8%, around 2 points above the SMT output BLEU score. The oracle score indicates that more than 6 BLEU points can still be gained if the postediting is only applied to the improvable subset of sentences from the test corpus. Introducing the out-of-domain LM with SP E m LM g+m leads to a BLEU score of 47.9%. The highest BLEU score obtained by an initial SMT (47.2% with the system T M g+m LM m ) is already overtaken by this last SPE system jointly used with the com SMT system. Since the oracle scores indicate that the highest gain can be reached by the SPE system with the interpolated LM, we will focus on this configuration for our experiments on sentence selection described in Section 6.

Out-of-Domain PBMT System

This section describes the post-editing of out-ofdomain PBMT system outputs, for which medical data are only employed to build LMs. For each LM used during the translation step, we evaluate the impact of the proposed SPE approach.

Out-of-Domain LM

The first evaluation of SPE on the out-of-domain PBMT system is done with T M g LM g relying only on out-of-domain data to build its statistical models. We introduce the in-domain data during the SPE step, in the SPE model, in the LM, or in both. The results are presented in Table 4. We can see

System

% BLEU (oracle) T M g LM g 29.9 + SP E m LM m 43.4 (44.2) + SP E m LM g+m 45.6 (47.0) Table 4: BLEU scores of SPE on the out-ofdomain PBMT system using an out-of-domain LM.

that introducing in-domain data during the postediting step increases the BLEU score of the translated test corpus. From a baseline at 29.9% of BLEU, the SPE systems lead to an absolute improvement of 13.5 and 15.7 points depending on the SPE data configuration. Using the interpolated LMs for the SPE system shows the highest BLEU score, both with a naive application of SPE or for the oracle score. Let use note that the difference between SP E m LM m and SP E m LM g+m is statistically significant since it is associated with a pvalue of 0.001. However, these results are lower than the BLEU score obtained by the specialized translation system (T M m LM m ) presented in Table 2.

In-Domain LM

The second evaluation of SPE on the outof-domain PBMT system concerns T M g LM m , where in-domain data are introduced during the SMT process through the LM. The baseline is 39.2% of BLEU and the results presented in Table 5 show that 3.5 BLEU points are gained by the SPE step with a system built on medical data only. We performed the pairwise comparisons with BLEU and the oracle score and observed that SP E m LM m is statistically equivalent to SP E m LM g+m with p > 0.1 for both metrics. Again, these results are lower than the BLEU score obtained by the specialized translation system (T M m LM m ) presented in Table 2.

System % BLEU (oracle) T M g LM m 39.2 + SP E m LM m 42.7 (44.2) + SP E m LM g+m 42.5 (44.4)
Table 5: BLEU scores of SPE on the out-ofdomain PBMT system using a medical LM.

In-Domain and Mixed PBMT Systems

After our experiments on the out-of-domain PBMT system using different LMs, we focus on the post-editing of in-domain PBMT system output. Two systems are studied here, one using only in-domain data (T M m LM m ) and the other using both out and in-domain data (T M g+m LM m ). For T M m LM m , the baseline BLEU score is 46.4% and none of the tested SPE configuration was able to increase this score. However, the oracle scores measured resp. at 47.4 % and 47.5 % with SP E m LM g+m and SP E m LM m show the potential improvement using SPE. This aspect motivates our sentence selection approach presented in Section 6.

As far as T M g+m LM m is concerned, the use of the interpolated LM in the post-editing step (SP E m LM g+m ) degrades the BLEU score by 0.8 point, while the use of the medical LM (SP E m LM m ) does not statistically improve the baseline BLEU measured before SPE. For both configuration, the oracle score shows that a significant gain is still possible.

Selecting Sentences for Post-Editing

Post-editing selected sentences is motivated by the oracle scores measured in Section 5. We propose to build a classifier in order to partition sentences according to the possible BLEU gain with SPE. To train such a classifier, we use the medical development corpus and compute for each sentence its associated ∆BLEU score comparing BLEU before and after SPE. It is a binary classification task: if the ∆BLEU score is positive, i.e. SPE improves the sentence, the sentence is labelled Class 1; otherwise, the sentence is tagged with Class 2. Figure 1 illustrates the general architecture of our system.

The classifier used in our experiments is a Support Vector Machine (SVM) [START_REF] Boser | A training algorithm for optimal margin classifiers[END_REF] based on a linear kernel. We use the implementation of libSVM [START_REF] Chang | LIB-SVM: A library for support vector machines[END_REF] in the WEKA [START_REF] Hall | The WEKA data mining software: An update[END_REF] environment [START_REF] El-Manzalawy | WLSVM: Integrating LibSVM into Weka Environment[END_REF]. The translated (by the MT system com) in-domain development set is used to build a sentence-level post-edition model. Each sentence of the training corpus is considered as a vector composed of n-grams (n ∈ [1; 3]).

We decided to apply the classification method to the highest oracle score observed in Section 5, i.e. the com translation system jointly used with a SP E m LM g+m post-editing step. The oracle score for this configuration reaches 53.5%, while the naive application of SPE leads to a BLEU score of 47.9%. The test set translated by com is classified using SVM, where each sentence is associated with a normalized score for each of the two classes. Using the translation reference, we evaluate the classifier in terms of recall and precision. The recall reaches 79.5% and the precision 40.1%. In order to evaluate the gain in terms of BLEU on the whole test set, we decide to post-edit sentences according to their Class 1 scores given by the SVM. This score is the probability to improve BLEU at the sentence level. The evaluation can be repeated individually for each 0.1 score span (is, only the sentences in this exact range are post-edited) and then cumulated over consecutive spans (cs, all sentences above the threshold are post-edited). The results are displayed in Figure 2.

The cumulated span evaluation shows that postediting the sentences above a prediction score of 0.8 reaches the highest BLEU score. With this configuration, 1 BLEU point is gained compared to the naive application of SPE (from 47.9% to 48.9% of BLEU). The amount of sentences in each class is increasing between 0.5 and 0.8. Only 60 sentences remain in Class 1 with a prediction score above 0.9. in each class is an important aspect of the classifier accuracy. Figure 3 shows TER [START_REF] Snover | A study of translation edit rate with targeted human annotation[END_REF] and inverted BLEU scores of Class 1 sentences with a classification score over 0.8, before and after post-editing. It clearly appears that there are more post-edited than translated sentences with a 100% BLEU score (0% inverted BLEU): resp. 47 and 11 sentences. Also among the 109 translated sentences with a 0% BLEU score, only half remains at this level after post-editing. The evaluation on the test set shows a general improvement using both metrics, as detailed in Table 6. These final results present the possible gain in terms of translation quality with SPE and a classification approach. The comparison between the SPE systems with and without classification shows that the combination of SPE and SVM is better than the naive application of SPE with p = 0.004. SMT + SPE + SVM TER 42.3 40.4 39.7 BLEU 44.9 47.9 48.9

Table 6: TER and BLEU scores on the test set after translation, post-editing and classification (with p(Class1) >= 0.8).

Conclusion and Future Work

In this paper, we have presented a phrase-based post-editing approach for specific domain adaptation. Our experiments show that an out-ofdomain translation system can be adapted a posteriori through a naive application of the proposed SPE approach. Oracle scores indicate that gains in terms of BLEU score are still possible, even with a PBMT system built on in-domain data and without introducing new data during the post-editing step. The highest BLEU score is obtained using GOOGLE TRANSLATE combined with an SPE system (SP E m LM g+m ) and a classification step. Compared to the baseline, the BLEU score is increased by 4 BLEU points. Compared to the best PBMT system (T M g+m LM g+m ) with 47.3% of BLEU, the score is increased by 1.6 BLEU points (with p = 0.001). In a future work, other metrics will be used to measure the translation quality at the sentence level. We also want to introduce more features into the classifier training set based on quality estimation techniques for our sentence selection approach, in order to better fill the gap between the current BLEU and the oracle score.

  c 2012 European Association for Machine Translation.

Figure 1 :

 1 Figure 1: Training of a SVM classifier using a translated corpus where each sentence is associated with its ∆BLEU class.

Figure 3 :

 3 Figure 3: TER and inverted BLEU sentences distribution measured on the test corpus when Class 1 probability is over 0.8.

Table 1 :

 1 . The 1 http://translate.google.com/ monolingual corpora are composed of the target language part of the sixth version of the News Commentary corpus taken from the Project Syndicate website 2 , and the Shuffled News Crawl corpus. All these corpora were made available for the 2011 Workshop on Machine Translation (WMT11) 3 . The bilingual data are used to build translation models, whereas the monolingual data are employed to train language models. Number of sentences and words for the out and the in-domain data used in our experiments.

	Corpus	Sentences Words
	Bilingual Training Data
	Europarl v6	1.8 M	50 M
	United Nations	12 M 300 M
	EMEA (Medical)	160 k	4 M
	Monolingual Training Data
	News Commentary v6	181 k	4 M
	Shuffled News from 2007 to 2011	25 M 515 M

Table 2 :

 2

	system	% BLEU p-value
	T M g LM g T M g LM g+m T M g LM m com T M m LM m T M g+m LM m T M g+m LM g+m	29.9 38.2 39.2 44.9 46.4 47.2 47.3	0.002 0.002 0.002 0.007 0.001 0.75

BLEU scores of the different initial SMT systems when translating the test corpus from the medical domain.

  The amount of training sentences

	% BLEU	42 43 44 45 46 47 48 49			sentences		dev is	dev cs	0 2 4 6 8 10 12 26 24 22 20 18 16 14	% sentences
		[	[	[	[	[	[	[	[	[	]
		; 0 . 1	0 . 2	0 . 3	0 . 4	0 . 5	0 . 6	0 . 7	0 . 8	0 . 9	1 . 0
	[ 0 . 0		Class 1 Probability Distribution
	Figure 2: BLEU scores and amount of sentences
	classified in Class 1 for individual (is) and cumu-
	lated (cs) spans obtained on the test corpus.

http://www.project-syndicate.org/

http://www.statmt.org/wmt11/

http://www.emea.europa.eu/