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Eliciting a corpus of word-aligned phrases for MT. Lori Levin, Alon Lavie, Erik Peterson Language Technologies Institute Carnegie Mellon University. Introduction. Problem: Building Machine Translation systems for languages with scarce resources: - PowerPoint PPT Presentation
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Eliciting a corpus of word-aligned phrases for MT
Lori Levin, Alon Lavie, Erik Peterson
Language Technologies Institute
Carnegie Mellon University
Introduction
• Problem: Building Machine Translation systems for languages with scarce resources:– Not enough data for Statistical MT and Example-
Based MT– Not enough human linguistic expertise for writing
rules
• Approach: – Elicit high quality, word-aligned data from bilingual
speakers– Learn transfer rules from the elicited data
Modules of the AVENUE/MilliRADD rule learning system and MT system
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
Outline
• Demo of elicitation interface
• Description of elicitation corpus
• Overview of automated rule learning
Demo of Elicitation Tool
• Speaker needs to be bilingual and literate: no other knowledge necessary
• Mappings between words and phrases: Many-to-many, one-to-none, many-to-none, etc.
• Create phrasal mappings• Fonts and character sets:
– Including Hindi, Chinese, and Arabic
• Add morpheme boundaries to target language• Add alternate translations• Notes and context
English-Chinese Example
English-Hindi Example
Spanish-Mapudungun Example
English-Arabic Example
Testing of Elicitation Tool
• DARPA Hindi Surprise Language Exercise
• Around 10 Hindi speakers
• Around 17,000 phrases translated and aligned– Elicitation corpus– NPs and PPs from Treebanked Brown Corpus
Elicitation Corpus: Basic Principles
• Minimal pairs
• Syntactic compositionality
• Special semantic/pragmatic constructions
• Navigation based on language typology and universals
• Challenges
Elicitation Corpus: Minimal Pairs
• Eng: I fell.
Sp: Caí
M: Tranün• Eng: You (John) fell.
Sp: Tu (Juan) caiste
M: Eymi tranimi (Kuan)• Eng: You (Mary) fell. ;;
Sp: Tu (María) caiste
M: Eymi tranimi (Maria)
• Eng: I am falling.
Sp: Estoy cayendo
M: Tranmeken• Eng: You (John) are falling.
Sp: Tu (Juan) estás cayendo
M: Eimi(Kuan) tranmekeymi
Mapudungun: Spoken by around one million people in Chile and Argentina.
Using feature vectors to detect minimal pairs
• np1:(subj-of cl1).pro-pers.hum.2.sg. masc.no-clusn.no-def.no-alien
• cl1:(subj np1).intr-ag.past.complete– Eng: You (John) fell. Sp: Tu (Juan) caiste M: Eymi tranimi (Kuan)
• np1:(subj-of cl1).pro-pers.hum.2.sg. fem.no-clusn.no-def.no-alien
• cl1:(subj np1).intr-ag.past.complete– Eng: You (Mary) fell. ;; Sp: Tu (María) caiste M: Eymi tranimi (Maria)
Feature vectors can be extracted from the output of a parser for English or Spanish. (Except for features that English and Spanish do not have…)
Syntactic Compositionality
– The tree – The tree fell.– I think that the tree fell.
• We learn rules for smaller phrases – E.g., NP
• Their root nodes become non-terminals in the rules for larger phrases.– E.g., S containing an NP
• Meaning of a phrase is predictable from the meanings of the parts.
Special Semantic and Pragmatic Constructions
• Meaning may not be compositional– Not predictable from the meanings of the parts
• May not follow normal rules of grammar.– Suggestion: Why not go?
• Word-for-word translation may not work. • Tend to be sources of MT mismatches
– Comparative: • English: Hotel A is [closer than Hotel B]• Japanese: Hoteru A wa [Hoteru B yori] [tikai desu] Hotel A TOP Hotel B than close is• “Closer than Hotel B” is a constituent in English, but “Hoteru B
yori tikai” is not a constituent in Japanese.
Examples of Semantic/Pragmatic Categories
• Speech Acts: requests, suggestions, etc.
• Comparatives and Equatives
• Modality: possibility, probability, ability, obligation, uncertainty, evidentiality
• Correllatives: (the more the merrier)
• Causatives
• Etc.
A Challenge: Combinatorics– Person (1, 2, 3, 4)– Number (sg, pl, du, paucal)– Gender/Noun Class (?)– Animacy (animate/inanimate)– Definiteness (definite/indefinite)– Proximity (near, far, very far, etc.)– Inclusion/exclusion
• Multiply with: tenses and aspects (complete, incomplete, real, unreal, iterative, habitual, present, past, recent past, future, recent future, non-past, non-future, etc.)
• Multiply with verb class: agentive intransitive, non-agentive intransitive, transitive, ditransitive, etc.
• (Case marking and agreement may vary with verb tense, verb class, animacy, definiteness, and whether or not object outranks subject in person or animacy.)
Solutions to Combinatorics
• Generate paradigms of feature vectors, and then automatically generate sentences to match each feature vector.
• Use known universals to eliminate features: e.g., Languages without plurals don’t have duals.
Other Challenges of Computer Based Elicitation
• Inconsistency of human translation and alignment
• Bias toward word order of the elicitation language– Need to provide discourse context for given and new
information
• How to elicit things that aren’t grammaticalized in the elicitation language:– Evidential: I see that it is raining/Apparently it is
raining/It must be raining. • Context: You are inside the house. Your friend comes in
wet.
Transfer Rule Formalism
Type information
Part-of-speech/constituent information
Alignments
x-side constraints
y-side constraints
xy-constraints,
e.g. ((Y1 AGR) = (X1 AGR))
;SL: the man, TL: der Mann
NP::NP [DET N] -> [DET N]((X1::Y1)(X2::Y2)
((X1 AGR) = *3-SING)((X1 DEF = *DEF)((X2 AGR) = *3-SING)((X2 COUNT) = +)
((Y1 AGR) = *3-SING)((Y1 DEF) = *DEF)((Y2 AGR) = *3-SING)((Y2 GENDER) = (Y1 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: use previously learned rules to add hierarchical structure
3. Seeded Version Space 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))
Compositionality
Initial 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))
Version Space 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))
Examples of Learned Rules{NP,14244}
;;Score:0.0429
NP::NP [N] -> [DET N]
(
(X1::Y2)
)
{NP,14434}
;;Score:0.0040
NP::NP [ADJ CONJ ADJ N] ->
[ADJ CONJ ADJ N]
(
(X1::Y1) (X2::Y2)
(X3::Y3) (X4::Y4)
)
{PP,4894};;Score:0.0470PP::PP [NP POSTP] -> [PREP NP]((X2::Y1)(X1::Y2))
Manual Transfer Rules: Example
;; PASSIVE OF SIMPLE PAST (NO AUX) WITH LIGHT VERB;; passive of 43 (7b){VP,28}VP::VP : [V V V] -> [Aux V]( (X1::Y2) ((x1 form) = root) ((x2 type) =c light) ((x2 form) = part) ((x2 aspect) = perf) ((x3 lexwx) = 'jAnA') ((x3 form) = part) ((x3 aspect) = perf) (x0 = x1) ((y1 lex) = be) ((y1 tense) = past) ((y1 agr num) = (x3 agr num)) ((y1 agr pers) = (x3 agr pers)) ((y2 form) = part))
Manual Transfer Rules: Example
; NP1 ke NP2 -> NP2 of NP1; Ex: jIvana ke eka aXyAya; life of (one) chapter ; ==> a chapter of life;{NP,12}NP::NP : [PP NP1] -> [NP1 PP]( (X1::Y2) (X2::Y1); ((x2 lexwx) = 'kA'))
{NP,13}NP::NP : [NP1] -> [NP1]( (X1::Y1))
{PP,12}PP::PP : [NP Postp] -> [Prep NP]( (X1::Y2) (X2::Y1))
NP
PP NP1
NP P Adj N
N1 ke eka aXyAya
N
jIvana
NP
NP1 PP
Adj N P NP
one chapter of N1
N
life