【正文】 Alignment templates ? EBMT from SMT: automatically learned translation lexicon ? Transferbased from SMT: automatically learned translation lexicon, transfer rules。 using LM。Machine translation (I) MT overview Ling 571 Fei Xia Week 9: 11/22/05 – 11/29/05 Plan for the rest of the quarter ? MT: – Part I: MT overview: 11/22 11/29 – Part II: Wordbased SMT: 12/112/6 – Next quarter: seminar on MT ? Starting with a real baseline system ? Improving the system in various ways ? Reading and presenting recent papers ? Project presentation: 12/8 Homework and Quizzes ? Hw 8: due on 11/23 (tomorrow) ? Hw 9: due on 12/6 ? Hw 10: presentation due on 12/8, report due on 12/13 ? Quiz 3: 11/29 ? Quiz 4: 12/6 Outline ? MT in a nutshell ? Major challenges in MT ? Major approaches ? Evaluation of MT systems MT in a nutshell Q1: what is the ultimate goal of translation? ? Translation: source language ? target language (S?T) ? Ultimate goal: find the “perfect” translation for text in S, thus allowing people to “appreciate” the text in S without knowing S: – Accuracy: faithful to S, including meaning, connotation, style, … – Fluency: the translation is as natural as an utterance in T. Q2: what are the perfect translations? What do “Accuracy” and “Fluency” mean? ? Ex1: Complement / downplayer (1) A: Your daughter was phenomenal. (2) B: No. She was just soso. ? Ex2: Greeting: how?s everything? – Old days: chi1 le5 ma5? (Have {you} eaten?) – 1980s now: fa1 le5 ma5? (Have (you) gotten rich?) – 2022s now: li2 le5 ma5? (Have (you) gotten divorced?) ? The answer: it depends Q3: Can human always get the perfect translations? ? Novels: Shakespeare, Cao Xueqin, … hidden messages: c1c0, c2 c0, c3 c0, c4 c0 c1? c2? c3? c4? ? Word play, jokes, puns: – What do prisoners use to call each other? – Cell phones. ? Concept gaps: double jeopardy, go Greek, fen sui, bei fen, …. ? Other constraints: lyrics, dubbing, poem. “Crazy English” by Richard Lederer ? Let?s face it: English is a crazy language. There is no egg in eggplant or ham in hamburger, neither apple nor pine in pineapple. ? When a house burns up, it burns down. You fill in a form by filling it out and an alarm clock goes off by going on. ? When the stars are out, they are visible, but when the lights are out, they are invisible. And why, when I wind up my watch, I start it, but when I wind up this essay, I end it? How to translate it? ? “Compound” words: Let?s face it: English is a crazy language. There is no egg in eggplant or ham in hamburger, neither apple nor pine in pineapple. ? Verb+particle: When a house burns up, it burns down. You fill in a form by filling it out and an alarm clock goes off by going on. ? Predicate+argument: When the stars are out, they are visible, but when the lights are out, they are invisible. And why, when I wind up my watch, I start it, but when I wind up this essay, I end it? Q4: Can machines be as good as humans in translation quality? ? We know there are things that even humans cannot translate “perfectly”. ? For things that humans can translate, will machines be ever as good as humans in translation quality? –Never say “never”. – Not in the near future. Q5: what is MT good for? ? Rough translation: web data ? Computeraided human translation ? Translation for limited domain ? Crosslingual IR ? Machine is better than human in: – Speed: much faster than humans – Memory: can easily memorize millions of word/phrase translations. – Manpower: machines are much cheaper than humans – Fast learner: it takes minutes or hours to build a new system. Erasable memory ? – Never plain, never get tired, … Q6: what?s the MT history? (Based on work by John Hutchins) ? Before the puter: In the mid 1930s, a FrenchArmenian Gees Artsrouni and a Russian Petr Troyanskii applied for patents for ?translating machines?. ? The pioneers (19471954): the first public MT demo was give