1. Challenges of Machine Translation CSC 5930 Machine Translation Fall 2012 Dr. Tom Way

2. Translation is hard Novels Word play, jokes, puns, hidden messages Concept gaps: go Greek, bei fen Other constraints: lyrics, dubbing, poem, …

3. Major challenges Getting the right words: –Choosing the correct root form –Getting the correct inflected form –Inserting “spontaneous” words Putting the words in the correct order: –Word order: SVO vs. SOV, … –Unique constructions: –Divergence

4. Lexical choice Homonymy/Polysemy: bank, run Concept gap: no corresponding concepts in another language: go Greek, go Dutch, fen sui, lame duck, … Coding (Concept  lexeme mapping) differences: –More distinction in one language: e.g., kinship vocabulary. –Different division of conceptual space:

5. Choosing the appropriate inflection Inflection: gender, number, case, tense, … Ex: –Number: Ch-Eng: all the concrete nouns: ch_book  book, books –Gender: Eng-Fr: all the adjectives –Case: Eng-Korean: all the arguments –Tense: Ch-Eng: all the verbs: ch_buy  buy, bought, will buy

6. Inserting spontaneous words Function words: –Determiners: Ch-Eng: ch_book  a book, the book, the books, books –Prepositions: Ch-Eng: … ch_November  … in November –Relative pronouns: Ch-Eng: … ch_buy ch_book de ch_person  the person who bought /book/ –Possessive pronouns: Ch-Eng: ch_he ch_raise ch_hand  He raised his hand(s) –Conjunction: Eng-Ch: Although S1, S2  ch_although S1, ch_but S2 –…–…

7. Inserting spontaneous words (cont) Content words: –Dropped argument: Ch-Eng: ch_buy le ma  Has Subj bought Obj? –Chinese First name: Eng-Ch: Jiang …  ch_Jiang ch_Zemin … –Abbreviation, Acronyms: Ch-Eng: ch_12 ch_big  the 12 th National Congress of the CPC (Communist Party of China) – …

8. Major challenges Getting the right words: –Choosing the correct root form –Getting the correct inflected form –Inserting “spontaneous” words Putting the words in the correct order: –Word order: SVO vs. SOV, … –Unique construction: –Structural divergence

9. Word order SVO, SOV, VSO, … VP + PP  PP VP VP + AdvP  AdvP + VP Adj + N  N + Adj NP + PP  PP NP NP + S  S NP P + NP  NP + P

10. “Unique” Constructions Overt wh-movement: Eng-Ch: –Eng: Why do you think that he came yesterday? –Ch: you why think he yesterday come ASP? –Ch: you think he yesterday why come? Ba-construction: Ch-Eng –She ba homework finish ASP  She finished her homework. –He ba wall dig ASP CL hole  He digged a hole in the wall. –She ba orange peel ASP skin  She peeled the orange’s skin.

11. Translation divergences Source and target parse trees (dependency trees) are not identical. Example: I like Mary  S: Marta me gusta a mi (‘Mary pleases me’)