This paper presents a generalized description of the characteristics and implications of two processes that enable Fluent Machines’ machine translation system, called EliMT (a term coined by Dr. Jamie Carbonell after the system’s inventor, Eli Abir). These two processes are (1) an automated cross-language database builder and (2) an n-gram connector.

LogoMedia Corporation offers a new multilingual machine translation system – LogoMedia Translate – based upon smaller applications, called “applets”, designed to perform a small group of related tasks and to provide services to other applets. Working together, applets provide comprehensive solutions that are more effective, easier to implement, and less costly to maintain. Version 2, released in 2002, provides a single set of cooperating user interfaces and translation engines from 6 vendors for English to and from Chinese (Simplified and Traditional) Japanese, Korean, French, Italian, German, Spanish, Portuguese, Russian, Polish, and Ukrainian.

Any-Language Communications has developed a novel semantics-oriented pre-market prototype system, based on the Theory of Universal Grammar, that uses the innate relationships of the words in a sensible sentence (the natural intelligence) to determine the true contextual meaning of all the words. The system is built on a class/category structure of language concepts and includes a weighted inheritance system, a number language word conversion, and a tailored genetic algorithm to select the best of the possible word meanings. By incorporating all of the language information within the dictionaries, the same semantic processing code is used to interpret any language. This approach is suitable for machine translation (MT), sophisticated text mining, and artificial intelligence applications. An MT system has been tested with English, French, German, Hindi, and Russian. Sentences for each of those languages have been successfully interpreted and proper translations generated.

Pre-market prototype - to be available commercially in the second or third quarter of 2003.

This paper presents a description of the well-known family of machine translation systems, PARS. PARS was developed in the USSR as long ago as in 1989, and, since then, it has passed a difficult way from a mainframe-based, somewhat bulky system to a modern PC-oriented product. At the same time, we understand but well that, as any machine translation software, PARS is not artificial intelligence, and it is only capable of generating what is called “draft translation”. It is certainly useful, but can by no means be considered a kind of substitution for a human translator whenever high-quality translation is required.

MSR-MT is an advanced research MT prototype that combines rule-based and statistical techniques with example-based transfer. This hybrid, large-scale system is capable of learning all its knowledge of lexical and phrasal translations directly from data. MSR-MT has undergone rigorous evaluation showing that, trained on a corpus of technical data similar to the test corpus, its output surpasses the quality of best-of-breed commercial MT systems.

NESPOLE! is a speech-to-speech machine translation research system designed to provide fully functional speech-to-speech capabilities within real-world settings of common users involved in e-commerce applications. The project is funded jointly by the European Commission and the US NSF. The NESPOLE! system uses a client-server architecture to allow a common user, who is browsing web-pages on the internet, to connect seamlessly in real-time to an agent of the service provider, using a video-conferencing channel and with speech-to-speech translation services mediating the conversation. Shared web pages and annotated images supported via a Whiteboard application are available to enhance the communication.

We will present the KANTOO machine translation environment, a set of software servers and tools for multilingual document production. KANTOO includes modules for source language analysis, target language generation, source terminology management, target terminology management, and knowledge source development (see Figure 1).

The paper discusses a number of important issues in speech-to-speech translation, including the key issue of level of integration of all components of such systems, based on our experience in the field since 1990. Section 1 discusses dimensions of the spoken translation problem, while current and near term approaches to spoken translation are treated in Sections 2 and 3. Section 2 describes our current expectation-based, speaker-independent, two-way translation systems, and Section 3 presents the advanced translation engine under development for handling spontaneous dialogs.