Automatic translation from one human language to another using computers, 
better known as machine translation (MT), is a long-standing goal of 
computer science.  Accurate translation requires a great deal of knowledge 
about the usage and meaning of words, the structure of phrases, the meaning 
of sentences, and which real-life situations are plausible.  For 
general-purpose translation, the amount of required knowledge is staggering, 
and it is not clear how to prioritize knowledge acquisition efforts.

Recently, there has been a fair amount of research into extracting 
translation-relevant knowledge automatically from very large bilingual 
texts.  For some language pairs, the size of these texts already reaches 
250 million words.  Over the past years, several statistical MT projects 
have appeared in North America, Europe, and Asia, and the literature is 
growing substantially.  This talk will cover the basic algorithms developed 
in this field, plus some of the latest empirical results.