Resource Acquisition for Syntax-based MT from Parsed Parallel data

Resource Acquisition for Syntax-based MT

from Parsed Parallel dataAlon Lavie, Alok Parlikar and Vamshi Ambati

Language Technologies InstituteCarnegie Mellon University

Research Goals• Long-term research agenda (since 2000) focused on

developing a unified framework for MT that addresses the core fundamental weaknesses of previous approaches:– Representation – explore richer formalisms that can capture

complex divergences between languages– Ability to handle morphologically complex languages– Methods for automatically acquiring MT resources from

available data and combining them with manual resources– Ability to address both rich and poor resource scenarios

• Main research funding sources: NSF (AVENUE and LETRAS projects) and DARPA (GALE)

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CMU Statistical Transfer (Stat-XFER) MT Approach

• Integrate the major strengths of rule-based and statistical MT within a common framework:– Linguistically rich formalism that can express complex and

abstract compositional transfer rules– Rules can be written by human experts and also acquired

automatically from data– Easy integration of morphological analyzers and generators– Word and syntactic-phrase correspondences can be

automatically acquired from parallel text– Search-based decoding from statistical MT adapted to find the

best translation within the search space: multi-feature scoring, beam-search, parameter optimization, etc.

– Framework suitable for both resource-rich and resource-poor language scenarios

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Stat-XFER MT Systems • General Stat-XFER framework under development for past

seven years• Systems so far:

– Chinese-to-English– Hebrew-to-English– Urdu-to-English– German-to-English– French-to-English– Hindi-to-English– Dutch-to-English– Mapudungun-to-Spanish

• In progress or planned:– Arabic-to-English– Brazilian Portuguese-to-English– Inupiaq-to-English– Hebrew-to-Arabic– Quechua-to-Spanish– Turkish-to-English

Stat-XFER FrameworkSourceInput

Preprocessing

Morphology

TransferEngine

TransferRules

BilingualLexicon

TranslationLattice

Second-StageDecoder

LanguageModel

WeightedFeatures

TargetOutputJune 20, 2008 5SSST-2

Transfer Engine

Language Model + Additional Features

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

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

Source Inputבשורה הבאה

Decoder

English Outputin 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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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)))

MT Resource Acquisition in Resource-rich Scenarios

• Scenario: Significant amounts of parallel-text at sentence-level are available– Parallel sentences can be word-aligned and parsed (at least on

one side, ideally on both sides)• Goal: Acquire both broad-coverage translation lexicons and

transfer rule grammars automatically from the data• Syntax-based translation lexicons:

– Broad-coverage constituent-level translation equivalents at all levels of syntactic granularity

– Can serve as the elementary building blocks for transfer trees constructed at runtime using the transfer rules

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Acquisition Process• Automatic Process for Extracting Syntax-driven Rules and

Lexicons from sentence-parallel data:1. Word-align the parallel corpus (GIZA++)2. Parse the sentences independently for both languages3. Run our new PFA Constituent Aligner over the parsed

sentence pairs4. Extract all aligned constituents from the parallel trees5. Extract all derived synchronous transfer rules from the

constituent-aligned parallel trees6. Construct a “data-base” of all extracted parallel

constituents and synchronous rules with their frequencies and model them statistically (assign them max-likelihood probabilities)

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PFA Constituent Node Aligner• Input: a bilingual pair of parsed and word-aligned sentences• Goal: find all sub-sentential constituent alignments between

the two trees which are translation equivalents of each other

• Equivalence Constraint: a pair of constituents <S,T> are considered translation equivalents if:– All words in yield of <S> are aligned only to words in yield of <T> (and

vice-versa)– If <S> has a sub-constituent <S1> that is aligned to <T1>, then <T1>

must be a sub-constituent of <T> (and vice-versa) • Algorithm is a bottom-up process starting from word-level,

marking nodes that satisfy the constraints

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PFA Node Alignment Algorithm

Each of the nodes stores a value.

All nodes are initialized with the value 1.

Each Word to Word alignment is assigned a unique prime number.


For every word to word alignment, we do the following:• Let p be the unique

prime value assigned to the alignment.

• Let ws and wt be the aligned words on the source and target side.

• Assign the value p to the POS nodes corresponding to the words ws and wt .

• Example: “Australia” gets value 2, “is” gets value 3.


In case there are “one-to-many” alignments, they are considered as multiple “one-to-one” alignments, and all of these alignments are given the same prime value.

Example: “North Korea” is just one word on Chinese side. That word is assigned the value 25, which is a product 5*5.


Once all the lexical items have values, we propogate the values up the tree as follows:

• Work bottom-up• A node updates its

value as the product of the values of its children.


Once all the lexical items have values, we propogate the values up the tree as follows:

• Work bottom-up• A node updates its

value as the product of the values of its children.

• Values could become large!


Once all nodes have values, they can be aligned as follows:

• If a node on Chinese side has a value same as node on English side, align them.

• If two nodes have equal values, take the node at lowest level in the tree.


Once all nodes have values, they can be aligned as follows:

• If a node on Chinese side has a value same as node on English side, align them.

• If two nodes have equal values, take the node at lowest level in the tree.


Features of the algorithm:

• Aligned constituents can have different labels

• Order of the sub-constituents does not matter in node alignment

• Unaligned words in constituents are allowed, but we are conservative (attach low).


Extraction of Phrases:

Get the yields of the aligned nodes and add them to a phrase table tagged with syntactic categories on source and target sides.

Example:

NP # NP :: 澳洲 # Australia


All Phrases from this tree:

1. IP # S :: 澳洲是与北韩有邦交的少数国家之一。 # Australia is one of the few countries that have diplomatic relations with North Korea .

2. VP # VP :: 是与北韩有邦交的少数国家之一 # is one of the few countries that have diplomatic relations with North Korea

3. NP # NP :: 与北韩有邦交的少数国家之一 # one of the few countries that have diplomatic relations with North Korea

4. VP # VP :: 与北韩有邦交 # have diplomatic relations with North Korea5. NP # NP :: 邦交 # diplomatic relations6. NP # NP :: 北韩 # North Korea7. NP # NP :: 澳洲 # Australia

PFA Constituent Node Alignment Performance

• Compare with manually-aligned constituent nodes:• Selected 30 sentences from Chinese-English parallel treebank• Bilingual expert manually aligned the nodes in the trees

• Main sources of disagreement: – 1-to-many and many-to-many word alignments– Errors or inconsistencies in the manual word alignments

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Precision Recall F-1 F-0.50.8129 0.7325 0.7705 0.7841


• Evaluation Data: Chinese-English Treebank– Parallel Chinese-English Treebank with manual word-

alignments– 3342 Sentence Pairs

• Created a “Gold Standard” constituent alignments using the manual word-alignments and treebank trees– Node Alignments: 39874 (About 12/tree pair)– NP to NP Alignments: 5427

• Evaluation: Run PFA Aligner with automatic word alignments on same data and compare with the “gold Standard” alignments

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Viterbi Combination

Precision Recall F-1

Intersection 0.6382 0.5395 0.5847Union 0.8114 0.2915 0.4289Sym-1 (Thot Toolkit) 0.7142 0.4534 0.5547Sym-2 (Thot Toolkit) 0.7135 0.4631 0.5617Grow-Diag-Final 0.7777 0.3462 0.4791Grow-Diag-Final-and 0.6988 0.4700 0.5620

• Viterbi word alignments from Chinese-English and reverse directions were merged using different algorithms

• Tested the performance of Node-Alignment with each resulting alignment

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Transfer Rule Learning• Input: Constituent-aligned parallel trees• Idea: Aligned nodes act as possible decomposition points of

the parallel trees– The sub-trees of any aligned pair of nodes can be broken apart

at lower-level aligned nodes, creating an inventory of “tree-fragment” correspondences

– Synchronous “tree-frags” can be converted into synchronous rules

– Similar in nature to [Galley et al 2004, 2006]• Algorithm:

– Find all possible minimal tree fragment decompositions from the node aligned trees

– “Flatten” the tree fragments into Stat-XFER style synchronous CFG rules

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Rule Extraction Algorithm

Tree-fragment extraction:

Extract Sub-tree segments including synchronous alignment information in the target tree. All the sub-trees and the super-tree are extracted.


Flat Rule Creation:

Each of the tree fragment pairs is flattened to create a Rule in the “Stat-XFER” Formalism –

Four major parts to the rule:

1. Type of the rule: Source and Target side type information

2. Constituent sequence of the synchronous flat rule

3. Alignment information of the constituents

4. Constraints in the rule (Currently not extracted)


Flat Rule Creation:

Sample rule:

IP::S [ NP VP .] -> [NP VP .](

;; Alignments(X1::Y1)(X2::Y2)

;;Constraints)


Flat Rule Creation:

Sample rule:

NP::NP [VP 北 CD 有邦交 ] -> [one of the CD countries that VP]

(

;; Alignments(X1::Y7)(X3::Y4))

Note: 1. Any one-to-one aligned words

are elevated to Part-Of-Speech in flat rule.

2. Any non-aligned words on either source or target side remain lexicalized


All rules extracted: VP::VP [VC NP] -> [VBZ NP](;; Alignments(X1::Y1)(X2::Y2))

NP::NP [NR] -> [NNP](;; Alignments(X1::Y1)(X2::Y2))

VP::VP [ 北 NP VE NP] -> [ VBP NP with NP](;; Alignments(X2::Y4)(X3::Y1)(X4::Y2))

All rules extracted: NP::NP [VP 北 CD 有邦交 ] -> [one of the CD countries that VP](;; Alignments(X1::Y7)(X3::Y4))

IP::S [ NP VP ] -> [NP VP ](;; Alignments(X1::Y1)(X2::Y2))

NP::NP [ “ 北韩” ] -> [“North” “Korea”](;Many to one alignment is a phrase)

Chinese-English Rule Learning

• Transfer Rules:– 61 manually developed transfer rules– High-accuracy rules extracted from manually word-

aligned parallel data

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Translation Example• SrcSent 3 澳洲是与北韩有邦交的少数国家之一。• Gloss: Australia is with north korea have diplomatic relations DE few country world• Reference: Australia is one of the few countries that have diplomatic relations with

North Korea.

• Translation: Australia is one of the few countries that has diplomatic relations with north korea .

• Overall: -5.77439, Prob: -2.58631, Rules: -0.66874, TransSGT: -2.58646, TransTGS: -1.52858, Frag: -0.0413927, Length: -0.127525, Words: 11,15

• ( 0 10 "Australia is one of the few countries that has diplomatic relations with north korea" -5.66505 " 澳洲是与北韩有邦交的少数国家之一 " "(S1,1124731 (S,1157857 (NP,2 (NB,1 (LDC_N,1267 'Australia') ) ) (VP,1046077 (MISC_V,1 'is') (NP,1077875 (LITERAL 'one') (LITERAL 'of') (NP,1045537 (NP,1017929 (NP,1 (LITERAL 'the') (NUMNB,2 (LDC_NUM,420 'few') (NB,1 (WIKI_N,62230 'countries') ) ) ) (LITERAL 'that') (VP,1021811 (LITERAL 'has') (FBIS_NP,11916 'diplomatic relations') ) ) (FBIS_PP,84791 'with north korea') ) ) ) ) ) ")

• ( 10 11 "." -11.9549 " 。 " "(MISC_PUNC,20 '.')")

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Example: XFER Rules

;;SL::(2,4) 对台贸易 ;;TL::(3,5) trade to taiwan ;;Score::22{NP,1045537}NP::NP [PP NP ] -> [NP PP ]((*score* 0.916666666666667)(X2::Y1)(X1::Y2))

;;SL::(2,7) 直接提到伟哥的广告 ;;TL::(1,7) commercials that directly mention the name viagra ;;Score::5{NP,1017929}NP::NP [VP " 的 " NP ] -> [NP "that" VP ]((*score* 0.111111111111111)(X3::Y1)(X1::Y3))

;;SL::(4,14) 有一至多个高新技术项目或产品 ;;TL::(3,14) has one or more new , high level technology projects or products ;;Score::4{VP,1021811}VP::VP [" 有 " NP ] -> ["has" NP ]((*score* 0.1)(X2::Y2))

Current and Future Work• Extraction based on both trees or trees on one side (with

projection)?– Trees on both side provide accurate constituent boundaries,

but divergent parser representations results in large coverage gaps

– Compromise: trees on one side + low-level constituents (chunks) on the other side…

• Exploring the space of extracted rules:– Binarize the rules or not?– Collapse constituent categories (or refine some of them)?– Rule filtering strategies (keep only count > 1 ?)– Rule scoring strategies (currently only max likelihood scores)

• Refining word alignment errors• Merging of resources acquired from data with manual

lexicons and transfer rulesJune 20, 2008 SSST-2 33

Conclusions• Stat-XFER is a promising general MT framework, suitable to

a variety of MT scenarios and languages• Provides a complete solution for building end-to-end MT

systems from parallel data, akin to phrase-based SMT systems (training, tuning, runtime system)

• Syntactic resources acquired from parallel corpora may be useful for other types of MT systems (high quality phrase tables)

• Complex but highly interesting set of open research issues

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Resource Acquisition for Syntax-based MT from Parsed Parallel data