wrapper syntax for example-based machine translation karolina owczarzak, bart mellebeek, declan...
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Wrapper Syntax for Example-Based Machine Translation
Karolina Owczarzak, Bart Mellebeek, Declan Groves, Josef Van Genabith, Andy Way
National Centre for Language TechnologySchool of Computing
Dublin City University
Overview
• TransBooster – wrapper technology for MT– motivation– decomposition process– variables and template contexts– recomposition
• Example-Based Machine Translation– marker-based EBMT
• Experiment– English-Spanish– Europarl, Wall Street Journal section of Penn II Treebank– automatic and manual evaluation
• Comparison with previous experiments
TransBooster – wrapper technology for MT
• Assumption:
MT systems perform better at translating short sentences than long ones.
• Decompose long sentences into shorter and syntactically simpler chunks, send to translation, recompose on output
• Decomposition linguistically guided by syntactic parse of the sentence
TransBooster – wrapper technology for MT
• TransBooster technology is universal and can be applied to any MT system
• Experiments to date:– TB and Rule-Based MT (Mellebeek et al.,
2005a,b)– TB and Statistical MT (Mellebeek et al., 2006a)– TB and Multi-Engine MT (Mellebeek et al.,
2006b)
• TransBooster outperforms baseline MT systems
TransBooster – decomposition
• Input – syntactically parsed sentence (Penn II format)• Decompose into pivot and satellites
– pivot: usually main predicate (plus additional material)– satellites: arguments and adjuncts
• Recursively decompose satellites if longer than x leaves
• Replace satellites around pivot with variables– static: simple same-type phrases with known translation– dynamic: simplified version of original satellites– send off to translation
• Insert each satellite into a template context– static: simple predicate with known translation– dynamic: simpler version of original clause (pivot +
simplified arguments, no adjuncts)– send off to translation
TransBooster – decomposition example
(S (NP (NP (DT the) (NN chairman)) (, ,) (NP (NP (DT a) (JJ long-time) (NN rival)) (PP (IN of) (NP (NNP Bill) (NNP Gates)))) (, ,)) (VP (VBZ likes) (NP (ADJP (JJ fast) (CC and) (JJ confidential)) (NNS deals))) (. .))
[The chairman, a long-time rival of Bill Gates,]ARG1 [likes]pivot [fast and confidential deals]ARG2.
[The chairman]V1 [likes]pivot [deals]V2.
[The chairman, a long-time rival of Bill Gates,]ARG1 [likes deals]V1.
[The chairman likes]V1 [fast and confidential deals]ARG2.
[The man]V1 [likes]pivot [cars]V2.
[The chairman, a long-time rival of Bill Gates,]ARG1 [is sleeping]V1.
[The man sees]V1 [fast and confidential deals]ARG2.
MT engine
TransBooster – recomposition
• MT output: a set of translations with dynamic and static variables and contexts for a sentence S
• Remove translations of dynamic variables and contexts from translation of S
• If unsuccessful, back off to translation with static variables and contexts, remove those
• Recombine translated pivot and satellites into output sentence
TransBooster – recomposition example
[The chairman]V1 [likes]pivot [deals]V2. ->El presidente tiene gusto de repartos.
[The chairman, a long-time rival of Bill Gates,]ARG1 [likes deals]V1. ->
El presidente, un rival de largo plazo de Bill Gates, tiene gusto de repartos.
[The chairman likes]V1 [fast and confidential deals]ARG2. ->
El presidente tiene gusto de repartos rápidos y confidenciales.
[The man]V1 [likes]pivot [cars]V2. ->
El hombre tiene gusto de automóviles.
[The chairman, a long-time rival of Bill Gates,]ARG1 [is sleeping]V1. ->
El presidente, un rival de largo plazo de Bill Gates, está durmiendo.
[The man sees]V1 [fast and confidential deals]ARG2. ->
El hombre ve repartos rápidos y confidenciales.
[El presidente, un rival de largo plazo de Bill Gates,] [tiene gusto de] [repartos rápidos y confidenciales].Original translation:El presidente, rival de largo plazo de Bill Gates, gustos ayuna y los repartos confidenciales.
The chairman, a long-time rival of Bill Gates, likes fast and confidential deals.
EBMT – Overview
• An aligned bilingual corpus
• Input text is matched against this corpus
• The best match is found and a translation is produced
French
F1
F2
F3
F4
EX (input)
search
F2 F4
FX (output)
English
E1
E2
E3
E4
Given in corpus
John went to school Jean est allé à l’école
The butcher’s is next to the baker’s
La boucherie est à côté de la boulangerie
Isolate useful fragments
John went to Jean est allé à
the baker’s la boulangerie
We can now translate
John went to the baker’s
Jean est allé à la boulangerie
EBMT – Marker-Based Chunking
<DET> = {the,a,these……} <DET> = {le,la,l’,une,un,ces…..}
<PREP> = {on, of …} <PREP> = {sur, d’ ..}
English phrase : on virtually all uses of asbestos
French translation: sur virtuellement tous usages d’asbeste
<PREP> on virtually <DET> all uses <PREP> of asbestos
<PREP> sur virtuellement <DET> tous usages <PREP> d’ asbeste
Marker Chunks:
<PREP> on virtually : sur virtuellement
<DET> all uses : tous usages
<PREP> of asbestos : d’asbeste
Lexical Chunks:
<LEX> on : sur <LEX> virtually : virtuellement
<LEX> all : tous <LEX> uses : usages
<LEX> of : d’ <LEX> asbestos : asbeste
Experiment
• English -> Spanish
• Two test sets:– Wall Street Journal section of Penn II Treebank
800 sentences– Europarl 800 sentences
• “Out-of-domain” factor:– TransBooster developed on perfect Penn II
trees– EBMT trained on 958K English-Spanish
Europarl sentences
Experiment – Results
Results for EBMT vs TransBooster on 741-sentence test set from Europarl.
Europarl BLEU NIST
EBMT 0.2111 5.9243
TransBooster 0.2134 5.9342
Percent of Baseline 101% 100.2%
Wall Street Journal
BLEU NIST
EBMT 0.1098 4.9081
TransBooster 0.1140 4.9321
Percent of Baseline 103.8% 100.5%
Results for EBMT vs TransBooster on 800-sentence test set from Penn II Treebank.
Automatic evaluation
Experiment - Results
Manual evaluation• 100 randomly selected sentences from EP test set:
– source English sentence– EBMT translation– EBMT + TransBooster translation
• 3 judges, native speakers of Spanish fluent in English• Accuracy and fluency: relative scale for comparing the two translations
Inter-judge agreement (Kappa): Fluency > 0.948, Accuracy > 0.926
Fluency Accuracy
TB > EBMT 35.33% 35%
EBMT > TB 16% 19.33%
Absolute quality gain when using TransBooster: Fluency 19.33% of sentences Accuracy 15.67% of sentences
Experiment – Results
TB improvements:
Example 1
Source: women have decided that they wish to work, that they wish to make their work
compatible with their family life.
EBMT: hemos decidido su deseo de trabajar, su deseo de hacer su trabajo compatible con su vida familiar. empresarias
TB: mujeres han decidido su deseo de trabajar, su deseo de hacer su trabajo
compatible con su vida familiar.Example 2
Source: if this global warming continues, then part of the territory of the eu member states will become sea or desert.
EBMT: si esto continúa calentamiento global, tanto dentro del territorio de los estados
miembros tendrán tornarse altamar o desértico
TB: si esto calentamiento global perdurará, entonces parte del territorio de los
estados miembros de la unión europea tendrán tornarse altamar o desértico
Previous experiments
TransBooster vs. SMT on 800-sentence test set from Europarl.
TB vs. SMT: EP BLEU NISTSMT 0.198
65.8393TransBooster 0.205
25.8766% of Baseline 103.3
%100.6%
TB vs. RBMT: WSJ
BLEU NISTRule-Based MT 0.310
87.3428TransBooster 0.316
37.3901% of Baseline 101.7
%100.6%Results for TransBooster vs. Rule-Based MT on 800-
sentence test set from Penn II Treebank.
TB vs. SMT: WSJ
BLEU NISTSMT 0.134
35.1432TransBooster 0.
13795.1259% of Baseline 102.7
%99.7%
TransBooster vs. SMT on 800-sentence test set from Penn II Treebank.
TB vs. EBMT: EP
BLEU NISTEBMT 0.211
15.9243TransBooster 0.213
45.9342% of Baseline 101% 100.2%TransBooster vs. EBMT on 800-sentence
test set
from Europarl.
TB vs. EBMT: WSJ
BLEU NISTEBMT 0.109
84.9081TransBooster 0.114
04.9321% of Baseline 103.8
%100.5%TransBooster vs. EBMT on 800-sentence
test set
from Penn II Treebank.
Previous experiments
TransBooster vs. SMT on 800-sentence test set from Europarl.
TB vs. SMT: EP BLEU NIST
SMT 0.1986 5.8393
TransBooster 0.2052 5.8766
% of Baseline 103.3% 100.6%
TB vs. EBMT: EP BLEU NIST
EBMT 0.2111 5.9243
TransBooster 0.2134 5.9342
% of Baseline 101% 100.2%
TransBooster vs. EBMT on 800-sentence test set from Europarl.
Previous experiments
TB vs. RBMT: WSJ BLEU NIST
Rule-Based MT 0.3108 7.3428
TransBooster 0.3163 7.3901
% of Baseline 101.7% 100.6%TransBooster vs. Rule-Based MT on 800-sentence test set from Penn II Treebank.
TB vs. SMT: WSJ BLEU NIST
SMT 0.1343 5.1432
TransBooster 0. 1379 5.1259
% of Baseline 102.7% 99.7%
TransBooster vs. SMT on 800-sentence test set from Penn II Treebank.
TB vs. EBMT: WSJ BLEU NISTEBMT 0.1098 4.9081TransBooster 0.1140 4.9321% of Baseline 103.8% 100.5%
TransBooster vs. EBMT on 800-sentence test set from Penn II Treebank.
Summary
• TransBooster is a universal technology to decompose and recompose MT text
• Net improvement in translation quality against EBMT:
Fluency 19.33% of sentences Accuracy 15.67% of sentences
• Successful experiments to date: rule-based MT, phrase-based SMT, multi-engine MT, EBMT
• Journal article in preparation