1. Challenges of Machine Translation
CSC Machine Translation
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 12th National Congress of the CPC (Communist Party of China) …

8. Major challenges Putting the words in the correct order:
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’)