This paper introduces the Multilingual Information Service System being implemented for the Beijing Olympics. Multilingual machine translation is an important component in this system. This real world application asks for advanced as well as mature and proven technologies, where MT is challenged. However, by appropriately choosing domain and scenario, current MT technologies are successfully integrated in the pilot system. Future applications ask MT to have better performance in readability, lexicon coverage and more efficient. Multilinguality support and fast language adaptation is highly desired by such real world systems.

Accurate and timely information on global public health issues is key to being able to quickly assess and respond to emerging health risks around the world. The Public Health Agency of Canada has developed the Global Public Health Intelligence Network (GPHIN). Information from GPHIN is provided to the WHO, international governments and non-governmental organizations who can then quickly react to public health incidents. GPHIN is a secure Internet-based “early warning” system that gathers preliminary reports of public health significance on a “real-time” basis, 24 hours a day, 7 days a week. This unique multilingual system gathers and disseminates relevant information on disease outbreaks and other public health events by monitoring global media sources such as news wires and web sites. This monitoring is done in eight languages with machine translation being used to translate non-English articles into English and English articles into the other languages. The information is filtered for relevancy by an automated process which is then complemented by human analysis. The output is categorized and made accessible to users. Notifications about public health events that may have serious public health consequences are immediately forwarded to users. GPHIN employs a “best-of-breed” approach when it comes to the selection of the machine translation ‘engines’. This philosophy ensures that the quality of the machine translation is the best available for whatever language pair selected. It also imposes some unique integration and operational problems. GPHIN has a broad scope. It tracks events such as disease outbreaks, infectious diseases, contaminated food and water, bio-terrorism and exposure to chemicals, natural disasters, and issues related to the safety of products, drugs and medical devices. GPHIN is managed by Health Canada’s Centre for Emergency Preparedness and Response (CEPR), which was created in July 2000 to serve as Canada’s central coordinating point for public health security. It is considered a centre of expertise in the area of civic emergencies including natural disasters and malicious acts with health repercussions. CEPR offers a number of practical supports to municipalities, provinces and territories, and other partners involved in first response and public health security. This is achieved through its network of public health, emergency health services, and emergency social services contacts.

In the last decade, the statistical approach has found widespread use in machine translation both for written and spoken language and has had a major impact on the translation accuracy. This paper will cover the principles of statistical machine translation and summarize the progress made so far.

Since 1994, China’s HTRDP machine translation evaluation has been conducted for five times. Systems of various translation directions between Chinese, English, Japanese and French have been tested. Both human evaluation and automatic evaluation are conducted in HTRDP evaluation. In recent years, the evaluation was organized jointly with NICT of Japan. This paper introduces some details of this evaluation.

MANOS (Multilingual Application Network for Olympic Services) project. aims to provide intelligent multilingual information services in 2008 Olympic Games. By narrowing down the general language technology, this paper gives an overview of our new work on Phrase-Based Statistical Machine Translation (PBT) under the framework of the MANOS. Starting with the construction of large scale Chinese-English corpus (sentence aligned) and introduction four methods to extract phrases, The promising results from PBT systems lead us to confidences for constructing a high-quality translation system and harmoniously integrate it into MANOS platform.

This paper reports on measures to improve the quality of MT systems, by using a hybrid system architecture which adds corpus-based and statistical components to an existing rule-based system backbone. The focus is on improving the accuracy of the dictionary resources.