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Learning with Structured Output

Learning with Structured Output

This video was recorded at SMART Workshop, Grenoble 2007. More than half of the EU citizens are not able to hold a conversation in a language other than their mother tongue, let alone to conduct a negotiation, or interpret a law. In a time of wide availability of communication technologies, language barriers are a serious bottleneck to European integration and to economic and cultural exchanges in general. More effective tools to overcome such barriers, in the form of software for machine translation and other cross-lingual textual information access tasks, are in strong demand. Statistical methods are promising, in that they achieve performances equivalent or superior to those of rule-based systems, at a fraction of the development effort. There are, however, some identified shortcomings in these methods, preventing their broad diffusion. As an example, even though lexical choice is usually more accurate with Statistical Machine Translation (SMT) systems than with their rule-based counterparts, the text they produce tends to be less fluent. As a second example, SMT systems are trained in batch mode and do not adapt by taking user feedback into account. Finally, in Cross-Language Information Retrieval tasks, query words are most often translated independent of one another, thus giving up possibly relevant contextual clues. SMART is an attempt to address these and other shortcomings by the methods of modern Statistical Learning. The scientific focus is on developing new and more effective statistical approaches while ensuring that existing know-how is duly taken into account. By bringing together leading research institutions in Statistical Learning, Machine Translation and Textual Information Access, the SMART consortium is well positioned to achieve this goal.

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