Despite considerable investment over the past 50 years, only a small number of language pairs is covered by MT systems designed for information access, and even fewer are capable of quality translation or speech translation. To open the door toward MT of adequate quality for all languages (at least in principle), we propose four keys. On the technical side, we should (1) dramatically increase the use of learning techniques which have demonstrated their potential at the research level, and (2) use pivot architectures, the most universally usable pivot being UNL. On the organizational side, the keys are (3) the cooperative development of open source linguistic resources on the Web, and (4) the construction of systems where quality can be improved "on demand" by users, either a priori through interactive disambiguation, or a posteriori by correcting the pivot representation through any language, thereby unifying MT, computer-aided authoring, and multilingual generation.

We propose a program of research which has as its goal establishing a framework and methodology for investigating the pragmatic aspects of the translation process and implementing a computational platform for carrying out systematic experiments on the pragmatics of translation. The program has four components. First, on the basis of a comparative study of multiple translations of the same document into a single target language, a pragmatics-based computational model is to be developed in which reasoning about the beliefs of the participants in the translation task and about the content of a text are central. Second, existing Natural Language Processing technologies are to be appraised as potential components of a computational platform that supports investigations into the effects of pragmatics on translation. Third, the platform is to be assembled and prototype translation systems implemented which conform to the pragmatics-based computational model of translation. Finally, a novel evaluation methodology is to be developed and evaluations of the systems carried out.

User feedback has often been proposed as a method for improving the accuracy of machine translation systems, but useful feedback can also serve a number of secondary benefits, including increasing user confidence in the MT technology and expanding the potential audience of users. Amikai, Inc. has produced a number of communication tools which embed translation technology and which attempt to improve the user experience by maximizing useful user interaction and feedback. As MT continues to develop, further attention needs to be paid to developing the overall user experience, which can improve the utility of translation tools even when translation quality itself plateaus.

In this short paper, I explore ways in which the MT community might formulate goals that will expand on known successes, build on existing strengths, and identify long term research goals.

Our focus is on high-quality (HQ) translation, the worldwide demand for which continues to increase exponentially and now far exceeds the capacity of the translation profession to satisfy it. To what extent is MT currently being used to satisfy this growing demand for HQ translation? Quite obviously, very little. Although MT is being used today by more people than ever before, very few of these users are professional translators. This represents a major change, for a mere ten years ago, translators were still the principal target market for most MT vendors. What happened to bring about this change? For that matter, what happened to most of those MT vendors? The view we present is that the most promising strategy for HQ MT is to embed MT systems in translation environments where the translator retains full control over their output. In our opinion, this new type of interactive MT will achieve better acceptance levels among translators and significantly improve the prospects of MT’s commercial success in the translation industry.

The performance of machine translation technology after 50 years of development leaves much to be desired. There is a high demand for well performing and cheap MT systems for many language pairs and domains, which automatically adapt to rapidly changing terminology. We argue that for successful MT systems it will be crucial to apply data-driven methods, especially statistical machine translation. In addition, it will be very important to establish common test environments. This includes the availability of large parallel training corpora, well defined test corpora and standardized evaluation criteria. Thereby research results can be compared and this will open the possibility for more competition in MT research.

NICE is a machine translation project for low-density languages. We are building a tool that will elicit a controlled corpus from a bilingual speaker who is not an expert in linguistics. The corpus is intended to cover major typological phenomena, as it is designed to work for any language. Using implicational universals, we strive to minimize the number of sentences that each informant has to translate. From the elicited sentences, we learn transfer rules with a version space algorithm. Our vision for MT in the future is one in which systems can be quickly trained for new languages by native speakers, so that speakers of minor languages can participate in education, health care, government, and internet without having to give up their languages.

In this paper, organized in essay style, I first assess the situation of Machine Translation, which is characterized, on the one hand, by unsatisfied user expectations, and, on the other hand, by an ever increasing need for translation technology to fulfil the promises of the global knowledge society, which is promoted by almost all governments and industries worldwide. The assessment is followed by an outline of the design of a blueprint that describes possible steps of an MT evolution regarding short term, mid term and long term developments. Although some user communities might aim at an MT revolution, the evolutionary implementation of the different aspects of the blueprint fit seamless with the foundation that we are faced with in the assessment part. With the blueprint the thesis of this MT evolution essay is established, and the stage is opened for the antithesis in which I develop the points for an MT revolution. Finally, in the synthesis part I develop a combined view which then completes the discussion and the establishment of a blueprint for MT evolution.

This paper discusses the challenges which Chinese-English machine translation (MT) systems face in translating personal names. We show that the translation of names between Chinese and English is complicated by different factors, including orthographic, phonetic, geographic and social ones. Four existing systems were tested for their capability in translating personal names from Chinese to English. Test data embodying geographic and sociolinguistic differences were obtained from a synchronous Chinese corpus of news media texts. It is obvious that systems vary considerably in their ability to identify personal names in the source language and render them properly in the target language. Given the criticality of personal name translation to the overall intelligibility of a translated text, the coverage of personal names should be one of the important criteria in the evaluation of MT performance. Moreover, name translation, which calls for a hybrid approach, would remain a central issue to the future development of MT systems, especially for online and real-time applications.