1. Machine Translation Nov 8, 2006
Thanks to Bonnie Dorr for some of these slides!!
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2. MT on the web
Babelfish:
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3. 3 methods for MT Direct Transfer Interlingua
against those of the teacher →
Yo comparé sus respuestas a las de la profesora
Rule: [check X against Y] → [comparar X a Y]
Transfer
Ich habe ihn gesehen → I have seen him
Rule: [clause agt aux obj pred] → [clause agt aux pred obj]
Interlingua
I like Mary→ Mary me gusta a mí
Rep: [BeIdent (I [ATIdent (I, Mary)] Like+ingly)]
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4. MT Challenges: Ambiguity
Syntactic Ambiguity I saw the man on the hill with the telescope
Lexical Ambiguity
E: book
S: libro, reservar
Semantic Ambiguity
Homography: ball(E) = pelota, baile(S)
Polysemy: kill(E), matar, acabar (S)
Semantic granularity esperar(S) = wait, expect, hope (E) be(E) = ser, estar(S) fish(E) = pez, pescado(S)
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5. Divergence Frequency Categorial (X tener hambre  X have hunger) [98%]
32% of sentences in UN Spanish/English Corpus (5K)
35% of sentences in TREC El Norte Corpus (19K)
Divergence Types
Categorial (X tener hambre  X have hunger) [98%]
Conflational (X dar puñaladas a Z  X stab Z) [83%]
Structural (X entrar en Y  X enter Y) [35%]
Head Swapping (X cruzar Y nadando  X swim across Y) [8%]
Thematic (X gustar a Y  Y like X) [6%]
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6. Spanish/Arabic Divergences
Divergence E/E’ (Spanish) E/E’ (Arabic)
Categorial be jealous when he returns
have jealousy [tener celos] upon his return [ﻋﻧﺩ ﺮﺠﻭﻋﻪ]
Conflational float come again
go floating [ir flotando] return [ﻋﺎﺪ]
Structural enter the house seek
enter in the house [entrar en la casa] search for [ﺒﺣﺙ ﻋﻦ]
Head Swap run in do something quickly
enter running [entrar corriendo] go-quickly in doing something [ﺍﺴﺭﻉ]
Thematic I have a headache
my-head hurts me [me duele la cabeza] —
[Arg1 [V]] Þ [Arg1 [MotionV] Modifier(v)]
“The boat floated’’ Þ “The boat went floating’’
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7. MT Challenges: Divergences
Meaning of two translationally equivalent phrases is distributed differently in the two languages
Example:
English: [RUN INTO ROOM]
Spanish: [ENTER IN ROOM RUNNING]
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8. Three MT Approaches: Direct, Transfer, Interlingual
This slide from Bonnie Dorr!
Original metaphor due to
Bernard Vauquois
Semantic
Composition
Semantic
Decomposition
Semantic
Structure
Semantic
Structure
Semantic
Analysis
Semantic
Generation
Semantic
Transfer
Syntactic
Structure
Syntactic
Structure
Syntactic
Transfer
Syntactic
Analysis
Syntactic
Generation
Word
Structure
Word
Structure
Direct
Morphological
Analysis
Morphological
Generation
Source Text
Target Text
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9. The Transfer Model
Idea: apply contrastive knowledge, i.e., knowledge about the difference between two languages
Steps:
Analysis: Syntactically parse Source language
Transfer: Rules to turn this parse into parse for Target language
Generation: Generate Target sentence from parse tree
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10. Transfer architecture
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11. English to French Generally English: Adjective Noun
French: Noun Adjective
Note: not always true
Route mauvaise ‘bad road, badly-paved road’
Mauvaise route ‘wrong road’)
But is a reasonable first approximation
Rule:
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12. Example: English to Japanese Transfer
Rule for Existential-there: delete “there” and convert
4th constituent to relative clause modifying the noun
Rule for relative clauses: reverse the order of them
Syntax is done: apply lexical transfer.
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13. English to Japanese Transfer
From “niqa no teire o suru ojiisan ita”
Add “ga” to mark subject
Chose verb to agree with subject
Inflect verbs
Linearize tree:
Niwa no teire o shite ita ojiisan ga ita
Garden GEN upkeep OBJ do PASTPROG old man SUBJ was
“There was an old man gardening”
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14. E-to-J Transfer: rules used
Existential-There-Sentence
There1 Verb2 NP3 Postnominal4
->
(NP -> NP3 Relative-Clause4) Verb2
NP -> Np1 Relative-Clause2
NP -> Relative-Clause2 NP1
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15. Lexical Transfer Man: Can treat like lexical ambiguity,
Ojisan ‘old man’
Man is the only linguistic animal ->
Ningen ‘man, human being’ Or
Hito ‘person, persons’
Can treat like lexical ambiguity,
Disambiguate during parsing
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16. Transfer: some problems
N2 sets of transfer rules!
Grammar and lexicon full of language-specific stuff
Hard to build, hard to maintain
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17. MT Method 2: Interlingua
Intuition: Instead of lg-lg knowledge rules, use the meaning of the sentence to help
Steps:
1) translate source sentence into meaning representation
2) generate target sentence from meaning.
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18. Interlingua for there was an old man gardening
EVENT: GARDENING
AGENT: MAN
NUMBER SG
DEFINITENESS INDEF
ASPECT: PROGRESSIVE
TENSE: PAST
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19. Interlingua
Idea is that some of the MT work that we need to do is part of other NLP tasks
E.g., disambiguating E:book S:‘libro’ from E:book S:‘reservar’
So we could have concepts like BOOKVOLUME and RESERVE and solve this problem once for each language
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20. Vauqois diagram
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21. Direct Translation Idea: more robust, word-specific models
Start with a Source language sentence
Write little transformations, directly on words, to turn it into a Target language sentence.
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22. Direct MT J-to-E Watashihatsukuenouenopenwojonniageta.
1. Morphological analysis
Watashi h tsukue no ue no pen wo jon ni ageru PAST
2) lexical transfer of content words
I ha desk no ue no pen wo John ni give PAST
3) various preposition work
I ha pen on desk wo John to give PAST.
4) SVO rearrangements
I give PAST pen on desk John to.
5) miscellany
I give PAST the pen on the desk to John.
6) morphological generation
I gave the pen on the desk to John.
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23. Direct MT stage 2, (ex. from Panov 1960 via Hutchins 1986)
Function direct-translate-much/many
If preceding word is ‘how’
Return skol’ko
Else if preceding word is ‘as’
Return skol’ko zhe
Else if word is ‘much’
If preceding words is ‘very’;
Return nil (not translated)
Else if following word is a noun
Return ‘mnogo’
Else /*word is many*/
If preceding word is PREP and following is NOUN
Return ‘mnogii’
Else return ‘mnogo’
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24. Three MT Approaches: Direct, Transfer, Interlingual
This slide from Bonnie Dorr!
Original metaphor due to
Bernard Vauquois
Semantic
Composition
Semantic
Decomposition
Semantic
Structure
Semantic
Structure
Semantic
Analysis
Semantic
Generation
Semantic
Transfer
Syntactic
Structure
Syntactic
Structure
Syntactic
Transfer
Syntactic
Analysis
Syntactic
Generation
Word
Structure
Word
Structure
Direct
Morphological
Analysis
Morphological
Generation
Source Text
Target Text
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25. 3 methods pros and cons
Thanks to Bonnie Dorr!
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26. Direct MT: pros and cons (Bonnie Dorr)
Fast
Simple
Cheap
No translation rules hidden in lexicon
Cons
Unreliable
Not powerful
Rule proliferation
Requires lots of context
Major restructuring after lexical substitution
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27. Interlingual MT: pros and cons (B. Dorr)
Avoids the N2 problem
Easier to write rules
Cons:
Semantics is HARD
Useful information lost (paraphrase)
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28. Summary Intro and a little history
Language Similarities and Divergences
Four main MT Approaches
Transfer
Interlingua
Direct
Statistical
Evaluation
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