Statistical Machine Translation: Final Report
Automatic translation from one human language to another using computers better known as machine translation MT is a longstanding goal of computer science In order to be able to perform such a task the computer must know the two languages synonyms for words and phrases grammars of the two languages and semantic or world knowledge One way to incorporate such knowledge into a computer is to use bilingual experts to hand craft the necessary information into the computer program Another is to let the computer learn some of these things automatically by examining large amounts of parallel text documents which are translations of each other The Canadian government produces one such resource for example in the form of parliamentary proceedings which are recorded in both English and French Recently statistical data analysis has been used to gather MT knowledge automatically from parallel bilingual text Unfortunately these techniques and tools have not been disseminated to the scienti c community in very usable form and new follow on ideas have developed sporadically In a six week summer workshop at Johns Hopkins University we constructed a basic statistical MT toolkit called Egypt intended for distribution to interested researchers We also used the toolkit as a platform for experimentation during the workshop Our experiments included working with distant language pairs such as Czech English rapidly porting to new language pairs managing with small bilingual data sets speeding up algorithms for decoding and bilingual and text training and incorporating morphology syntax dictionaries and cognates Late in the workshop we built an MT system for a new language pair Chinese English in a single day We describe both the toolkit and the experiments in this report