Nov 17, 2005Learning-based MT1 Learning-based MT Approaches for Languages with Limited Resources Alon Lavie Language Technologies Institute Carnegie Mellon

Nov 17, 2005 Learning-based MT 1

Learning-based MT Approaches for Languages with Limited

Resources

Alon LavieLanguage Technologies Institute

Carnegie Mellon University

Joint work with: Jaime Carbonell, Lori Levin, Kathrin Probst, Erik Peterson, Christian Monson, Ariadna Font-Llitjos, Alison Alvarez, Roberto Aranovich


Outline• Rationale for learning-based MT• Roadmap for learning-based MT• Framework overview• Elicitation• Learning transfer Rules• Automatic rule refinement• Learning Morphology• Example prototypes• Implications for MT with vast parallel data• Conclusions and future directions


Machine Translation: Where are we today?

• Age of Internet and Globalization – great demand for MT: – Multiple official languages of UN, EU, Canada, etc.– Documentation dissemination for large manufacturers

(Microsoft, IBM, Caterpillar)• Economic incentive is still primarily within a small

number of language pairs• Some fairly good commercial products in the market for

these language pairs– Primarily a product of rule-based systems after many years

of development• Pervasive MT between most language pairs still non-

existent and not on the immediate horizon


Mi chiamo Alon Lavie My name is Alon Lavie

Give-information+personal-data (name=alon_lavie)

[s [vp accusative_pronoun “chiamare” proper_name]]

[s [np [possessive_pronoun “name”]]

[vp “be” proper_name]]

Direct

Transfer

Interlingua

Analysis Generation

Approaches to MT: Vaquois MT Triangle


Progression of MT• Started with rule-based systems

– Very large expert human effort to construct language-specific resources (grammars, lexicons)

– High-quality MT extremely expensive only for handful of language pairs

• Along came EBMT and then SMT…– Replaced human effort with extremely large volumes of

parallel text data– Less expensive, but still only feasible for a small number of

language pairs– We “traded” human labor with data

• Where does this take us in 5-10 years?– Large parallel corpora for maybe 25-50 language pairs

• What about all the other languages?• Is all this data (with very shallow representation of

language structure) really necessary?• Can we build MT approaches that learn deeper levels of

language structure and how they map from one language to another?


Why Machine Translation for Languages with Limited Resources?

• We are in the age of information explosion– The internet+web+Google anyone can get the

information they want anytime…• But what about the text in all those other

languages?– How do they read all this English stuff?– How do we read all the stuff that they put online?

• MT for these languages would Enable:– Better government access to native indigenous and

minority communities– Better minority and native community participation in

information-rich activities (health care, education, government) without giving up their languages.

– Civilian and military applications (disaster relief)– Language preservation


The Roadmap to Learning-based MT

• Automatic acquisition of necessary language resources and knowledge using machine learning methodologies:– Learning morphology (analysis/generation)– Rapid acquisition of broad coverage word-to-word and

phrase-to-phrase translation lexicons– Learning of syntactic structural mappings

• Tree-to-tree structure transformations [Knight et al], [Eisner], [Melamed] require parse trees for both languages

• Learning syntactic transfer rules with resources (grammar, parses) for just one of the two languages

– Automatic rule refinement and/or post-editing• A framework for integrating the acquired MT resources

into effective MT prototype systems• Effective integration of acquired knowledge with

statistical/distributional information


CMU’s AVENUE Approach• Elicitation: use bilingual native informants to produce a

small high-quality word-aligned bilingual corpus of translated phrases and sentences– Building Elicitation corpora from feature structures– Feature Detection and Navigation

• Transfer-rule Learning: apply ML-based methods to automatically acquire syntactic transfer rules for translation between the two languages– Learn from major language to minor language– Translate from minor language to major language

• XFER + Decoder:– XFER engine produces a lattice of possible transferred

structures at all levels– Decoder searches and selects the best scoring combination

• Rule Refinement: refine the acquired rules via a process of interaction with bilingual informants

• Morphology Learning• Word and Phrase bilingual lexicon acquisition


AVENUE MT Approach Interlingua

Syntactic Parsing

Semantic Analysis

Sentence Planning

Text Generation

Source (e.g. Quechua)

Target(e.g. English)

Transfer Rules

Direct: SMT, EBMT

AVENUE: Automate Rule Learning


AVENUE Architecture

Learning Module

Transfer Rules

{PP,4894};;Score:0.0470PP::PP [NP POSTP] -> [PREP NP]((X2::Y1)(X1::Y2))

Translation Lexicon

Run Time Transfer System

Lattice Decoder

English Language Model

Word-to-Word Translation Probabilities

Word-aligned elicited data




Data Elicitation for Languages with Limited Resources

• Rationale:– Large volumes of parallel text not available create

a small maximally-diverse parallel corpus that directly supports the learning task

– Bilingual native informant(s) can translate and align a small pre-designed elicitation corpus, using elicitation tool

– Elicitation corpus designed to be typologically and structurally comprehensive and compositional

– Transfer-rule engine and new learning approach support acquisition of generalized transfer-rules from the data


Elicitation Tool: English-Chinese Example


Elicitation Tool:English-Chinese Example


Elicitation Tool:English-Hindi Example


Elicitation Tool:English-Arabic Example


Elicitation Tool:Spanish-Mapudungun Example


Designing Elicitation Corpora

• What do we want to elicit? – Diversity of linguistic phenomena and constructions– Syntactic structural diversity

• How do we construct an elicitation corpus?– Typological Elicitation Corpus based on elicitation and

documentation work of field linguists (e.g. Comrie 1977, Bouquiaux 1992): initial corpus size ~1000 examples

– Structural Elicitation Corpus based on representative sample of English phrase structures: ~120 examples

• Organized compositionally: elicit simple structures first, then use them as building blocks

• Goal: minimize size, maximize linguistic coverage


Typological Elicitation Corpus

• Feature Detection– Discover what features exist in the language and

where/how they are marked• Example: does the language mark gender of nouns?

How and where are these marked?– Method: compare translations of minimal pairs –

sentences that differ in only ONE feature

• Elicit translations/alignments for detected features and their combinations

• Dynamic corpus navigation based on feature detection: no need to elicit for combinations involving non-existent features


Typological Elicitation Corpus

• Initial typological corpus of about 1000 sentences was manually constructed

• New construction methodology for building an elicitation corpus using:– A feature specification: lists inventory of available

features and their values– A definition of the set of desired feature structures

• Schemas define sets of desired combinations of features and values

• Multiplier algorithm generates the comprehensive set of feature structures

– A generation grammar and lexicon: NLG generator generates NL sentences from the feature structures


Structural Elicitation Corpus• Goal: create a compact diverse sample corpus of

syntactic phrase structures in English in order to elicit how these map into the elicited language

• Methodology:– Extracted all CFG “rules” from Brown section of Penn

TreeBank (122K sentences)– Simplified POS tag set– Constructed frequency histogram of extracted rules– Pulled out simplest phrases for most frequent rules for NPs,

PPs, ADJPs, ADVPs, SBARs and Sentences– Some manual inspection and refinement

• Resulting corpus of about 120 phrases/sentences representing common structures

• See [Probst and Lavie, 2004]




Transfer Rule Formalism

Type informationPart-of-speech/constituent

informationAlignments

x-side constraints

y-side constraints

xy-constraints, e.g. ((Y1 AGR) = (X1 AGR))

;SL: the old man, TL: ha-ish ha-zaqen

NP::NP [DET ADJ N] -> [DET N DET ADJ]((X1::Y1)(X1::Y3)(X2::Y4)(X3::Y2)

((X1 AGR) = *3-SING)((X1 DEF = *DEF)((X3 AGR) = *3-SING)((X3 COUNT) = +)

((Y1 DEF) = *DEF)((Y3 DEF) = *DEF)((Y2 AGR) = *3-SING)((Y2 GENDER) = (Y4 GENDER)))


Transfer Rule Formalism (II)

Value constraints

Agreement constraints

;SL: the old man, TL: ha-ish ha-zaqen

NP::NP [DET ADJ N] -> [DET N DET ADJ]((X1::Y1)(X1::Y3)(X2::Y4)(X3::Y2)

((X1 AGR) = *3-SING)((X1 DEF = *DEF)((X3 AGR) = *3-SING)((X3 COUNT) = +)

((Y1 DEF) = *DEF)((Y3 DEF) = *DEF)((Y2 AGR) = *3-SING)((Y2 GENDER) = (Y4 GENDER)))


Rule Learning - Overview

• Goal: Acquire Syntactic Transfer Rules• Use available knowledge from the source

side (grammatical structure)• Three steps:

1. Flat Seed Generation: first guesses at transfer rules; flat syntactic structure

2. Compositionality Learning: use previously learned rules to learn hierarchical structure

3. Constraint Learning: refine rules by learning appropriate feature constraints


Flat Seed Rule Generation

Learning Example: NP

Eng: the big apple

Heb: ha-tapuax ha-gadol

Generated Seed Rule:

NP::NP [ART ADJ N] [ART N ART ADJ]

((X1::Y1)

(X1::Y3)

(X2::Y4)

(X3::Y2))


Flat Seed Rule Generation

• Create a “flat” transfer rule specific to the sentence pair, partially abstracted to POS– Words that are aligned word-to-word and have the

same POS in both languages are generalized to their POS

– Words that have complex alignments (or not the same POS) remain lexicalized

• One seed rule for each translation example• No feature constraints associated with seed

rules (but mark the example(s) from which it was learned)


Compositionality LearningInitial Flat Rules: S::S [ART ADJ N V ART N] [ART N ART ADJ V P ART N]

((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2) (X4::Y5) (X5::Y7) (X6::Y8))

NP::NP [ART ADJ N] [ART N ART ADJ]

((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2))

NP::NP [ART N] [ART N]

((X1::Y1) (X2::Y2))

Generated Compositional Rule:

S::S [NP V NP] [NP V P NP]

((X1::Y1) (X2::Y2) (X3::Y4))


Compositionality Learning

• Detection: traverse the c-structure of the English sentence, add compositional structure for translatable chunks

• Generalization: adjust constituent sequences and alignments

• Two implemented variants:– Safe Compositionality: there exists a transfer rule

that correctly translates the sub-constituent– Maximal Compositionality: Generalize the rule if

supported by the alignments, even in the absence of an existing transfer rule for the sub-constituent


Constraint LearningInput: Rules and their Example Sets

S::S [NP V NP] [NP V P NP] {ex1,ex12,ex17,ex26}

((X1::Y1) (X2::Y2) (X3::Y4))

NP::NP [ART ADJ N] [ART N ART ADJ] {ex2,ex3,ex13}

((X1::Y1) (X1::Y3) (X2::Y4) (X3::Y2))

NP::NP [ART N] [ART N] {ex4,ex5,ex6,ex8,ex10,ex11}

((X1::Y1) (X2::Y2))

Output: Rules with Feature Constraints:

S::S [NP V NP] [NP V P NP]

((X1::Y1) (X2::Y2) (X3::Y4)

(X1 NUM = X2 NUM)

(Y1 NUM = Y2 NUM)

(X1 NUM = Y1 NUM))


Constraint Learning

• Goal: add appropriate feature constraints to the acquired rules

• Methodology:– Preserve general structural transfer– Learn specific feature constraints from example set

• Seed rules are grouped into clusters of similar transfer structure (type, constituent sequences, alignments)

• Each cluster forms a version space: a partially ordered hypothesis space with a specific and a general boundary

• The seed rules in a group form the specific boundary of a version space

• The general boundary is the (implicit) transfer rule with the same type, constituent sequences, and alignments, but no feature constraints


Constraint Learning: Generalization

• The partial order of the version space:Definition: A transfer rule tr1 is strictly more general than another transfer rule tr2 if all f-structures that are satisfied by tr2 are also satisfied by tr1.

• Generalize rules by merging them:– Deletion of constraint– Raising two value constraints to an agreement

constraint, e.g. ((x1 num) = *pl), ((x3 num) = *pl) ((x1 num) = (x3 num))


Automated Rule Refinement

• Bilingual informants can identify translation errors and pinpoint the errors

• A sophisticated trace of the translation path can identify likely sources for the error and do “Blame Assignment”

• Rule Refinement operators can be developed to modify the underlying translation grammar (and lexicon) based on characteristics of the error source:– Add or delete feature constraints from a rule– Bifurcate a rule into two rules (general and specific)– Add or correct lexical entries

• See [Font-Llitjos, Carbonell & Lavie, 2005]




Morphology Learning• Goal: Unsupervised learning of morphemes and their

function from raw monolingual data– Segmentation of words into morphemes– Identification of morphological paradigms (inflections and

derivations)– Learning association between morphemes and their

function in the language• Organize the raw data in the form of a network of

paradigm candidate schemes• Search the network for a collection of schemes that

represent true morphology paradigms of the language• Learn mappings between the schemes and

features/functions using minimal pairs of elicited data• Construct analyzer based on the collection of schemes

and the acquired function mappings


Ø.sblamesolve

Example Vocabulary

blame blamed blames roamed

roaming roams solve solves solving

Ø.s.dblame

sblameroamsolve

e.esblamsolv

me.mesbla

e.esblamsolv

e.edblam

esblamsolv

Ø.s.dblame

Ø.sblamesolve

Øblameblamesblamedroams

roamedroaming

solvesolvessolving

e.es.edblam

edblamroam

dblameroame

Ø.dblame

s.dblame

sblameroamsolve

es.edblam

eblamsolv

me.mesbla

me.medbla

mesbla

me.mes.medbla

medblaroa

mes.medbla

mebla

37

a.as.o.os43

african, cas, jurídic, l, ...

a.as.o.os.tro1

cas

a.as.os50

afectad, cas, jurídic, l, ...

a.as.o59

cas, citad, jurídic, l, ...

a.o.os105

impuest, indonesi, italian, jurídic, ...

a.as199

huelg, incluid, industri,

inundad, ...

a.os134

impedid, impuest, indonesi,

inundad, ...

as.os68

cas, implicad, inundad, jurídic, ...

a.o214

id, indi, indonesi,

inmediat, ...

as.o85

intern, jurídic, just, l, ...

a.tro2

cas.cen

a1237

huelg, ib, id, iglesi, ...

as404

huelg, huelguist, incluid,

industri, ...

os534

humorístic, human, hígad,

impedid, ...

o1139

hub, hug, human,

huyend, ...

tro16

catas, ce, cen, cua, ...

as.o.os54

cas, implicad, jurídic, l, ...

o.os268

human, implicad, indici,

indocumentad, ...

Spanish Newswire Corpus

40,011 Tokens

6,975 Types

38

a.as.o.os43


a.as.o.os.tro1

cas

a.as.os50


a.as.o59


a.o.os105


a.as199


inundad, ...

a.os134


inundad, ...

as.os68


a.o214

id, indi, indonesi,

inmediat, ...

as.o85


a.tro2

cas.cen

a1237


as404


industri, ...

os534


impedid, ...

o1139

hub, hug, human,

huyend, ...

tro16


as.o.os54


o.os268


indocumentad, ...

C-Suffixes

C-Stems

Level 5 = 5 C-suffixes

C-Stem Type Count

39

a.as.o.os43


a.as.o.os.tro1

cas

a.tro2

cas.cen

tro16


Adjective Inflection Class

40

a.as.os50


a.as.o59


a.o.os105


a.as199


inundad, ...

a.os134


inundad, ...

as.os68


a.o214

id, indi, indonesi,

inmediat, ...

as.o85


a1237


as404


industri, ...

os534


impedid, ...

o1139

hub, hug, human,

huyend, ...

as.o.os54


o.os268


indocumentad, ...

From the spurious c-suffix “tro”

a.as.o.os.tro1

cas

a.tro2

cas.cen

tro16


a.as.o.os43


a.as.os50


a.as.o59


a.o.os105


a.as199


inundad, ...

a.os134


inundad, ...

as.os68


a.o214

id, indi, indonesi,

inmediat, ...

as.o85


a1237


as404


industri, ...

os534


impedid, ...

o1139

hub, hug, human,

huyend, ...

as.o.os54


o.os268


indocumentad, ...

41

De

cre

asin

g C

-Ste

m C

oun

t

Incr

ea

sin

g C

-Su

ffix

Co

unt

Basic Search Procedure




AVENUE Prototypes

• General XFER framework under development for past three years

• Prototype systems so far:– German-to-English, Dutch-to-English– Chinese-to-English– Hindi-to-English– Hebrew-to-English

• In progress or planned:– Mapudungun-to-Spanish– Quechua-to-Spanish– Arabic-to-English– Native-Brazilian languages to Brazilian Portuguese


Challenges for Hebrew MT

• Paucity in existing language resources for Hebrew– No publicly available broad coverage morphological

analyzer– No publicly available bilingual lexicons or dictionaries– No POS-tagged corpus or parse tree-bank corpus for

Hebrew– No large Hebrew/English parallel corpus

• Scenario well suited for CMU transfer-based MT framework for languages with limited resources


Hebrew-to-English MT Prototype

• Initial prototype developed within a two month intensive effort

• Accomplished:– Adapted available morphological analyzer– Constructed a preliminary translation lexicon– Translated and aligned Elicitation Corpus– Learned XFER rules– Developed (small) manual XFER grammar as a point

of comparison– System debugging and development– Evaluated performance on unseen test data using

automatic evaluation metrics

Transfer Engine

English Language Model

Transfer Rules{NP1,3}NP1::NP1 [NP1 "H" ADJ] -> [ADJ NP1]((X3::Y1) (X1::Y2) ((X1 def) = +) ((X1 status) =c absolute) ((X1 num) = (X3 num)) ((X1 gen) = (X3 gen)) (X0 = X1))

Translation Lexicon

N::N |: ["$WR"] -> ["BULL"]((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "BULL"))

N::N |: ["$WRH"] -> ["LINE"]((X1::Y1) ((X0 NUM) = s) ((Y0 lex) = "LINE"))

Source Input

בשורה הבאה

Decoder

English Output

in the next line

Translation Output Lattice

(0 1 "IN" @PREP)(1 1 "THE" @DET)(2 2 "LINE" @N)(1 2 "THE LINE" @NP)(0 2 "IN LINE" @PP)(0 4 "IN THE NEXT LINE" @PP)

Preprocessing

Morphology


Morphology Example

• Input word: B$WRH

0 1 2 3 4 |--------B$WRH--------| |-----B-----|$WR|--H--| |--B--|-H--|--$WRH---|


Morphology ExampleY0: ((SPANSTART 0) Y1: ((SPANSTART 0) Y2: ((SPANSTART 1) (SPANEND 4) (SPANEND 2) (SPANEND 3) (LEX B$WRH) (LEX B) (LEX $WR) (POS N) (POS PREP)) (POS N) (GEN F) (GEN M) (NUM S) (NUM S) (STATUS ABSOLUTE)) (STATUS ABSOLUTE))

Y3: ((SPANSTART 3) Y4: ((SPANSTART 0) Y5: ((SPANSTART 1) (SPANEND 4) (SPANEND 1) (SPANEND 2) (LEX $LH) (LEX B) (LEX H) (POS POSS)) (POS PREP)) (POS DET))

Y6: ((SPANSTART 2) Y7: ((SPANSTART 0) (SPANEND 4) (SPANEND 4) (LEX $WRH) (LEX B$WRH) (POS N) (POS LEX)) (GEN F) (NUM S) (STATUS ABSOLUTE))


Sample Output (dev-data)

maxwell anurpung comes from ghana for israel four years ago and since worked in cleaning in hotels in eilat

a few weeks ago announced if management club hotel that for him to leave israel according to the government instructions and immigration police

in a letter in broken english which spread among the foreign workers thanks to them hotel for their hard work and announced that will purchase for hm flight tickets for their countries from their money


Evaluation Results

• Test set of 62 sentences from Haaretz newspaper, 2 reference translations

System BLEU NIST P R METEOR

No Gram 0.0616 3.4109 0.4090 0.4427 0.3298

Learned 0.0774 3.5451 0.4189 0.4488 0.3478

Manual 0.1026 3.7789 0.4334 0.4474 0.3617


Hebrew-English: Test Suite Evaluation

Grammar BLEU METEOR

Baseline (NoGram) 0.0996 0.4916

Learned Grammar 0.1608 0.5525

Manual Grammar 0.1642 0.5320




Implications for MT with Vast Amounts of Parallel Data

• Learning word/short-phrase translations vs. learning long phrase-to-phrase translations

• Phrase-to-phrase MT ill suited for long-range reorderings ungrammatical output

• Recent work on hierarchical Stat-MT [Chiang, 2005] and parsing-based MT [Melamed et al, 2005]

• Learning general tree-to-tree syntactic mappings is equally problematic:– Meaning is a hybrid of complex, non-compositional phrases

embedded within a syntactic structure– Some constituents can be translated in isolation, others

require contextual mappings



• Our approach for learning transfer rules is applicable to the large data scenario, subject to solutions for several challenges:– No elicitation corpus break-down parallel

sentences into reasonable learning examples– Working with less reliable automatic word alignments

rather than manual alignments– Effective use of reliable parse structures for ONE

language (i.e. English) and automatic word alignments in order to decompose the translation of a sentence into several compositional rules.

– Effective scoring of resulting very large transfer grammars, and scaled up transfer + decoding



• Example: 他经常与江泽民总统通电话 He freq with J Zemin Pres via phone

He freq talked with President J Zemin over the phone



• Example: 他经常与江泽民总统通电话 He freq with J Zemin Pres via phone

He freq talked with President J Zemin over the phone

NP1

NP1

NP2

NP2

NP3

NP3


Conclusions• There is hope yet for wide-spread MT between many of

the worlds language pairs• MT offers a fertile yet extremely challenging ground for

learning-based approaches that leverage from diverse sources of information:– Syntactic structure of one or both languages– Word-to-word correspondences– Decomposable units of translation– Statistical Language Models

• Provides a feasible solution to MT for languages with limited resources

• Extremely promising approach for addressing the fundamental weaknesses in current corpus-based MT for languages with vast resources


Future Research Directions• Automatic Transfer Rule Learning:

– In the “large-data” scenario: from large volumes of uncontrolled parallel text automatically word-aligned

– In the absence of morphology or POS annotated lexica

– Learning mappings for non-compositional structures– Effective models for rule scoring for

• Decoding: using scores at runtime• Pruning the large collections of learned rules

– Learning Unification Constraints

• Integrated Xfer Engine and Decoder– Improved models for scoring tree-to-tree mappings,

integration with LM and other knowledge sources in the course of the search


Future Research Directions

• Automatic Rule Refinement• Morphology Learning• Feature Detection and Corpus

Navigation• …



Mapudungun-to-Spanish Example

Mapudungun

pelafiñ Maria

Spanish

No vi a María

English

I didn’t see Maria


Mapudungun-to-Spanish Example

Mapudungun

pelafiñ Mariape -la -fi -ñ Mariasee -neg -3.obj -1.subj.indicative Maria

Spanish

No vi a MaríaNo vi a Maríaneg see.1.subj.past.indicative acc Maria

English

I didn’t see Maria


V

pe

pe-la-fi-ñ Maria


V

pe

pe-la-fi-ñ Maria

VSuff

laNegation = +


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffGPass all features up


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fiobject person = 3


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffGPass all features up from both children


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

person = 1number = sgmood = ind


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

Pass all features up from both children

VSuffG


V

V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

Pass all features up from both children

VSuffGCheck that:1) negation = +2) tense is undefined


V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

V NP

N

Maria

N person = 3number = sghuman = +


Pass features up from

V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

Check that NP is human = +V VP


V

pe

Transfer to Spanish: Top-Down

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

Pass all features to Spanish side


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

Pass all features down


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

Pass object features down


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

Accusative marker on objects is introduced because human = +


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

VP::VP [VBar NP] -> [VBar "a" NP]( (X1::Y1)

(X2::Y3)

((X2 type) = (*NOT* personal)) ((X2 human) =c +)

(X0 = X1) ((X0 object) = X2)

(Y0 = X0)

((Y0 object) = (X0 object))(Y1 = Y0)(Y3 = (Y0 object))((Y1 objmarker person) = (Y3 person))((Y1 objmarker number) = (Y3 number))((Y1 objmarker gender) = (Y3 ender)))


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

V“no”

Pass person, number, and mood features to Spanish Verb

Assign tense = past


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

V“no”

Introduced because negation = +


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

V“no”

ver


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

V“no”

vervi

person = 1number = sgmood = indicativetense = past


V

pe


VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

V“no”

vi N

María

N

Pass features over to Spanish side


V

pe

I Didn’t see Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

NP

N

Maria

N

S

V

VP

S

VP

NP“a”V

V“no”

vi N

María

N


Documents

Nov 17, 2005Learning-based MT1 Learning-based MT Approaches for Languages with Limited Resources Alon Lavie Language Technologies Institute Carnegie Mellon