AVENUE/LETRAS: Learning-based MT Approaches for Languages with Limited Resources

Aug 28, 2006 Learning-based MT with Limited Resources

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AVENUE/LETRAS: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


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


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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


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


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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


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The Transfer EngineAnalysis

Source text is parsed into its grammatical structure. Determines transfer application ordering.

Example:

他看书。 (he read book)

S

NP VP

N V NP

他看书

TransferA target language tree is created by reordering, insertion, and deletion.

S

NP VP

N V NP

he read DET N

a book

Article “a” is inserted into object NP. Source words translated with transfer lexicon.

GenerationTarget language constraints are checked and final translation produced.

E.g. “reads” is chosen over “read” to agree with “he”.

Final translation:

“He reads a book”


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The Transfer Engine

• Some Unique Features:– Works with either learned or manually-

developed transfer grammars– Handles rules with or without unification

constraints– Supports interfacing with servers for

Morphological analysis and generation– Can handle ambiguous source-word

analyses and/or SL segmentations represented in the form of lattice structures


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The Lattice Decoder

• Simple Stack Decoder, similar in principle to SMT/EBMT decoders

• Searches for best-scoring path of non-overlapping lattice arcs

• Scoring based on log-linear combination of scoring components (no MER training yet)

• Scoring components:– Standard trigram LM– Fragmentation: how many arcs to cover the entire

translation?– Length Penalty– Rule Scores (not fully integrated yet)


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


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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


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Elicitation Tool: English-Chinese Example


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Elicitation Tool:English-Chinese Example


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Elicitation Tool:English-Hindi Example


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Elicitation Tool:English-Arabic Example


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Elicitation Tool:Spanish-Mapudungun Example


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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


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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


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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


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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]


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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)))


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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)))


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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


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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))


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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)


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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))


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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


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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))


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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


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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))


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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]


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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– Portuguese-to-English

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


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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


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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


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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


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Morphology Example

• Input word: B$WRH

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


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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))


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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


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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


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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


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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


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Implications for MT with Vast Amounts of Parallel Data

• 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] [Knight et al]

• 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


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• Our approach for learning transfer rules is applicable to the large data scenario, subject to solutions for several large 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


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• 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


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• 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


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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

• AVENUE’s XFER approach provides a feasible solution to MT for languages with limited resources

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


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Future Research Directions• Automatic Transfer Rule Learning:

– 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– In the “large-data” scenario: from large volumes of

uncontrolled parallel text automatically word-aligned– In the absence of morphology or POS annotated

lexica

• 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


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Future Research Directions

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

Navigation• …


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Mapudungun-to-Spanish Example

Mapudungun

pelafiñ Maria

Spanish

No vi a María

English

I didn’t see Maria


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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


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V

pe

pe-la-fi-ñ Maria


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V

pe

pe-la-fi-ñ Maria

VSuff

laNegation = +


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V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffGPass all features up


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V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fiobject person = 3


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V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffGPass all features up from both children


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V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

person = 1number = sgmood = ind


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V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

Pass all features up from both children

VSuffG


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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


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V

pe

pe-la-fi-ñ Maria

VSuff

la

VSuffG VSuff

fi

VSuffG VSuff

ñ

VSuffG

V NP

N

Maria

N person = 3number = sghuman = +


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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


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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


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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


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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


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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


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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 = +


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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)))


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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


70

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 = +


71

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


72

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


73

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


74

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


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Documents

AVENUE/LETRAS: Learning-based MT Approaches for Languages with Limited Resources