Challenges of Machine Translation

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Challenges of Machine Translation CSC 4598 Machine Translation Dr. Tom Way

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

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

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:

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

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 …

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) …

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

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

“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.

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’)