Post on 31-Jan-2016
description
Building NLP Systems for Two Resource Scarce Indigenous
Languages: Mapudungun and Quechua, and some other
languages
Christian Monson, Ariadna Font Llitjós, Roberto Aranovich, Lori Levin, Ralf
Brown, Erik Peterson, Jaime Carbonell, and Alon Lavie
Omnivorous MT
• Eat whatever resources are available
• Eat large or small amounts of data
Mapusaurus RoseaeMapu = landMapuche = land peopleMapudungun= land speech
AVENUE’s Inventory
• Resources– Parallel corpus– Monolingual corpus– Lexicon– Morphological
Analyzer (lemmatizer)– Human Linguist– Human non-linguist
• Techniques– Rule based transfer
system– Example Based MT– Morphology Learning– Rule Learning– Interactive Rule
Refinement– Multi-Engine MT
This research was funded in part by NSF grant number IIS-0121-631.
Startup without corpus or linguist
Requires someone who is bilingual and literate
The Elicitation Tool has been used with these languages
• Mapudungun• Hindi• Hebrew• Quechua• Aymara• Thai• Japanese• Chinese• Dutch• Arabic
Purpose of Elicitation
• Provide a small but highly targeted corpus of hand aligned data– To support machine
learning from a small data set
– To discover basic word order
– To discover how syntactic dependencies are expressed
– To discover which grammatical meanings are reflected in the morphology or syntax of the language
srcsent: Tú caístetgtsent: eymi ütrünagimialigned: ((1,1),(2,2))context: tú = Juan [masculino, 2a persona del
singular]comment: You (John) fell
srcsent: Tú estás cayendotgtsent: eymi petu ütünagimialigned: ((1,1),(2 3,2 3))context: tú = Juan [masculino, 2a persona del
singular]comment: You (John) are falling
srcsent: Tú caíste tgtsent: eymi ütrunagimialigned: ((1,1),(2,2))context: tú = María [femenino, 2a persona del
singular]comment: You (Mary) fell
Feature Structuressrcsent: Mary was not a leader.context: Translate this as though it were spoken to a peer co-
worker;
((actor ((np-function fn-actor)(np-animacy anim-human)(np- biological-gender bio-gender-female) (np-general-type proper-noun-type)(np-identifiability identifiable)(np- specificity specific)…))
(pred ((np-function fn-predicate-nominal)(np-animacy anim- human)(np-biological-gender bio-gender-female) (np- general-type common-noun-type)(np-specificity specificity- neutral)…))
(c-v-lexical-aspect state)(c-copula-type copula-role)(c-secondary-type secondary-copula)(c-solidarity solidarity-neutral) (c-v-grammatical-aspect gram-aspect-neutral)(c-v-absolute-tense past) (c-v-phase-aspect phase-aspect-neutral) (c-general-type declarative-clause)(c-polarity polarity-negative)(c-my-causer-intentionality intentionality-n/a)(c-comparison-type comparison-n/a)(c-relative-tense relative-n/a)(c-our-boundary boundary-n/a)…)
Current Work
• Search space:– Elements of meanings that might be
expressed by syntax or morphology: tense, aspect, person, number, gender, causation, evidentiality, etc.
– Syntactic dependencies: subject, object– Interactions of features:
• Tense and person • Tense and interrogative mood• Etc.
Current Work
• For a new language– For each item of the search space
• Eliminate it as irrelevant or• Explore it
– Using as few sentences as possible
Mar 1, 2006
Tools for Creating Elicitation Corpora
List of semantic features and values
The Corpus
Feature Maps: which combinations of features and values are of interest
…Clause-Level
Noun-Phrase
Tense & Aspect Modality
Feature Structure Sets
Feature Specification
Reverse Annotated Feature Structure Sets: add English sentences
Smaller CorpusSampling
XML SchemaXSLT Script
Mar 1, 2006
Tools for Creating Elicitation Corpora
List of semantic features and values
The Corpus
Feature Maps: which combinations of features and values are of interest
…Clause-Level
Noun-Phrase
Tense & Aspect Modality
Feature Structure Sets
Feature Specification
Reverse Annotated Feature Structure Sets: add English sentences
Smaller CorpusSampling
Combination Formalism
Mar 1, 2006
Tools for Creating Elicitation Corpora
List of semantic features and values
The Corpus
Feature Maps: which combinations of features and values are of interest
…Clause-Level
Noun-Phrase
Tense & Aspect Modality
Feature Structure Sets
Feature Specification
Reverse Annotated Feature Structure Sets: add English sentences
Smaller CorpusSampling
Feature Structure Viewer
Mar 1, 2006
Tools for Creating Elicitation Corpora
List of semantic features and values
The Corpus
Feature Maps: which combinations of features and values are of interest
…Clause-Level
Noun-Phrase
Tense & Aspect Modality
Feature Structure Sets
Feature Specification
Reverse Annotated Feature Structure Sets: add English sentences
Smaller CorpusSampling
Outline
• Two ideas– Omnivorous MT– Startup for low resource situation
• Four Languages– Mapudungun– Quechua– Hindi– Hebrew
The Avenue Low Resource Scenario
Learning
Module
Learned Transfer
Rules
Lexical Resources
Run Time Transfer System
Decoder
Translation
Correction
Tool
Word-Aligned Parallel Corpus
Elicitation Tool
Elicitation Corpus
Elicitation Rule Learning
Run-Time System
Rule Refinement
Rule
Refinement
Module
Morphology
Morphology Analyzer
Learning Module Handcrafted
rules
INPUT TEXT
OUTPUT TEXT
The Avenue Low Resource Scenario
Learning
Module
Learned Transfer
Rules
Lexical Resources
Run Time Transfer System
Decoder
Translation
Correction
Tool
Word-Aligned Parallel Corpus
Elicitation Tool
Elicitation Corpus
Elicitation Rule Learning
Run-Time System
Rule Refinement
Rule
Refinement
Module
Morphology
Morphology Analyzer
Learning Module Handcrafted
rules
INPUT TEXT
OUTPUT TEXT
The Avenue Low Resource Scenario
Learning
Module
Learned Transfer
Rules
Lexical Resources
Run Time Transfer System
Decoder
Translation
Correction
Tool
Word-Aligned Parallel Corpus
Elicitation Tool
Elicitation Corpus
Elicitation Rule Learning
Run-Time System
Rule Refinement
Rule
Refinement
Module
Morphology
Morphology Analyzer
Learning Module Handcrafted
rules
INPUT TEXT
OUTPUT TEXT
The Avenue Low Resource Scenario
Learning
Module
Learned Transfer
Rules
Lexical Resources
Run Time Transfer System
Decoder
Translation
Correction
Tool
Word-Aligned Parallel Corpus
Elicitation Tool
Elicitation Corpus
Elicitation Rule Learning
Run-Time System
Rule Refinement
Rule
Refinement
Module
Morphology
Morphology Analyzer
Learning Module Handcrafted
rules
INPUT TEXT
OUTPUT TEXT
Mapudungun Language
• 900,000 Mapuche people• At least 300.000 speakers of Mapudungun• Polysynthetic
sl: pe- rke- fi- ñ Maria ver-REPORT-3pO-1pSgS/INDtl: DICEN QUE LA VI A MARÍA (They say that) I saw Maria.
AVENUE Mapudungun
• Joint project between Carnegie Mellon University, the Chilean Ministry of Education, and Universidad de la Frontera.
Mapudungun to Spanish Resources
• Initially: – Large team of native speakers at Universidad de la Frontera,
Temuco, Chile• Some knowledge of linguistics• No knowledge of computational linguistics
– No corpus– A few short word lists– No morphological analyzer
• Later: Computational Linguists with non-native knowledge of Mapudungun
• Other considerations:– Produce something that is useful to the community, especially for
bilingual education– Experimental MT systems are not useful
Mapudungun
Learning
Module
Learned Transfer
Rules
Lexical Resources
Run Time Transfer System
Decoder
Translation
Correction
Tool
Word-Aligned Parallel Corpus
Elicitation Tool
Elicitation Corpus
Elicitation Rule Learning
Run-Time System
Rule Refinement
Rule
Refinement
Module
Morphology
Morphology Analyzer
Learning Module Handcrafted
rules
INPUT TEXT
OUTPUT TEXT
Corpus: 170 hours of spoken Mapudungun
Example Based MT
Spelling checker
Spanish Morphology from UPC, Barcelona
Mapudungun Products
• http://www.lenguasamerindias.org/– Click: traductor mapudungún– Dictionary lookup (Mapudungun to Spanish)– Morphological analysis– Example Based MT (Mapudungun to Spanish)
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
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
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)))
AVENUE Hebrew
• Joint project of Carnegie Mellon University and University of Haifa
Hebrew Language
• Native language of about 3-4 Million in Israel• Semitic language, closely related to Arabic and with
similar linguistic properties– Root+Pattern word formation system– Rich verb and noun morphology– Particles attach as prefixed to the following word: definite article
(H), prepositions (B,K,L,M), coordinating conjuction (W), relativizers ($,K$)…
• Unique alphabet and Writing System– 22 letters represent (mostly) consonants– Vowels represented (mostly) by diacritics– Modern texts omit the diacritic vowels, thus additional level of
ambiguity: “bare” word word– Example: MHGR mehager, m+hagar, m+h+ger
Hebrew Resources
• Morphological analyzer developed at Technion
• Constructed our own Hebrew-to-English lexicon, based primarily on existing “Dahan” H-to-E and E-to-H dictionary
• Human Computational Linguists
• Native Speakers
Hebrew
Learning
Module
Learned Transfer
Rules
Lexical Resources
Run Time Transfer System
Decoder
Translation
Correction
Tool
Word-Aligned Parallel Corpus
Elicitation Tool
Elicitation Corpus
Elicitation Rule Learning
Run-Time System
Rule Refinement
Rule
Refinement
Module
Morphology
Morphology Analyzer
Learning Module Handcrafted
rules
INPUT TEXT
OUTPUT TEXT
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 Learning
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))
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))
Quechua facts• Agglutinative language
• A stem can often have 10 to 12 suffixes, but it can have up to 28 suffixes
• Supposedly clear cut boundaries, but in reality several suffixes change when followed by certain other suffixes
• No irregular verbs, nouns or adjectives
• Does not mark for gender
• No adjective agreement
• No definite or indefinite articles (‘topic’ and ‘focus’ markers perform a similar task of articles and intonation in English or Spanish)
Quechua examples
– taki+ni (also written takiniy)sing 1sg (I sing) canto
– taki+sha+ni (takishaniy)sing progr 1sg (I am singing) estoy cantando
– taki+pa+ku+q+chu? taki sing -pa+ku to join a group to do something -q agentive -chu interrogative
(para) cantar con la gente (del pueblo)? (to sing with the people (of the village)?)
Quechua Resources
• A few native speakers, not linguists
• A computational linguist learning Quechua
• Two fluent, but non-native linguists
Quechua
Learning
Module
Learned Transfer
Rules
Lexical Resources
Run Time Transfer System
Decoder
Translation
Correction
Tool
Word-Aligned Parallel Corpus
Elicitation Tool
Elicitation Corpus
Elicitation Rule Learning
Run-Time System
Rule Refinement
Rule
Refinement
Module
Morphology
Morphology Analyzer
Learning Module Handcrafted
rules
INPUT TEXT
OUTPUT TEXT
Parallel Corpus: OCR with correction
Grammar rules;taki+sha+ni -> estoy cantando (I am singing){VBar,3} VBar::VBar : [V VSuff VSuff] -> [V V]( (X1::Y2)
((x0 person) = (x3 person)) ((x0 number) = (x3 number)) ((x2 mood) =c ger) ((y2 mood) = (x2 mood)) ((y1 form) =c estar) ((y1 person) = (x3 person)) ((y1 number) = (x3 number)) ((y1 tense) = (x3 tense))((x0 tense) = (x3 tense))((y1 mood) = (x3 mood))((x3 inflected) =c +)((x0 inflected) = +))
lex = cantarmood = ger
lex = estarperson = 1number = sgtense = presmood = ind
SpanishMorphologyGeneration
estoy
cantando
Hindi Resources
• Large statistical lexicon from the Linguistic Data Consortium (LDC)
• Parallel Corpus from LDC• Morphological Analyzer-Generator from LDC• Lots of native speakers• Computational linguists with little or no
knowledge of Hindi• Experimented with the size of the parallel corpus
– Miserly and large scenarios
Hindi
Learning
Module
Learned Transfer
Rules
Lexical Resources
Run Time Transfer System
Decoder
Translation
Correction
Tool
Word-Aligned Parallel Corpus
Elicitation Tool
Elicitation Corpus
Elicitation Rule Learning
Run-Time System
Rule Refinement
Rule
Refinement
Module
Morphology
Morphology Analyzer
Learning Module Handcrafted
rules
INPUT TEXT
OUTPUT TEXT
15,000 Noun Phrases from Penn TreeBank
Parallel Corpus
EBMT
SMT
Supported by DARPA TIDES
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
System BLEU M-BLEU NIST
EBMT 0.058 0.165 4.22
SMT 0.093 0.191 4.64
XFER (naïve) man
grammar
0.055 0.177 4.46
XFER (strong) no grammar
0.109 0.224 5.29
XFER (strong) learned
grammar
0.116 0.231 5.37
XFER (strong) man
grammar
0.135 0.243 5.59
XFER+SMT
0.136 0.243 5.65
Very miserly training data.
Seven combinations of components
Strong decoder allows re-ordering
Three automatic scoring metrics
Hindi-English
Extra Slides
The Avenue Low Resource Scenario
Learning
Module
Learned Transfer
Rules
Lexical Resources
Run Time Transfer System
Decoder
Translation
Correction
Tool
Word-Aligned Parallel Corpus
Elicitation Tool
Elicitation Corpus
Elicitation Rule Learning
Run-Time System
Rule Refinement
Rule
Refinement
Module
Morphology
Morphology Analyzer
Learning Module Handcrafted
rules
INPUT TEXT
OUTPUT TEXT
Feature Specification
• Defines Features and their values
• Sets default values for features
• Specifies feature requirements and restrictions
• Written in XML
Feature SpecificationFeature: c-copula-type
(a copula is a verb like “be”; some languages do not have copulas)Values
copula-n/a Restrictions: 1. ~(c-secondary-type secondary-copula)Notes:
copula-role Restrictions: 1. (c-secondary-type secondary-copula)Notes: 1. A role is something like a job or a function. "He is a teacher" "This is a vegetable peeler"
copula-identity Restrictions: 1. (c-secondary-type secondary-copula)Notes: 1. "Clark Kent is Superman" "Sam is the teacher"
copula-location Restrictions: 1. (c-secondary-type secondary-copula)Notes: 1. "The book is on the table" There is a long list of locative relations later in the feature specification.
copula-description Restrictions: 1. (c-secondary-type secondary-copula)Notes: 1. A description is an attribute. "The children are happy." "The books are long."
Feature Maps
• Some features interact in the grammar– English –s reflects person and number of the subject and tense of
the verb.– In expressing the English present progressive tense, the auxiliary
verb is in a different place in a question and a statement:• He is running.
• Is he running?
• We need to check many, but not all combinations of features and values.
• Using unlimited feature combinations leads to an unmanageable number of sentences
Evidentiality Map
Lexical Aspect
Assertiveness
Polarity
Source
Tense
Gram.
Aspect
activity-accomplishment
Assertiveness-asserted, Assetiveness-neutral
Polarity-positive, Polarity-negative
Hearsay, quotative, inferred, assumption
Visual, Auditory, non-visual-or-auditory
Past Present, Future Past Present
Perfective, progressive, habitual, neutral
habitual, neutral, progressive
Perfective, progressive, habitual, neutral
habitual, neutral, progressive
Current Work
• Navigation– Start: large search space of all possible
feature combinations– Finish: each feature has been eliminated as
irrelevant or has been explored– Goal: dynamically find the most efficient path
through the search space for each language.
Current Work
• Feature Detection– Which features have an effect on
morphosyntax?– What is the effect?– Drives the Navigation process
Feature Detection: Spanish
The girl saw a red book.((1,1)(2,2)(3,3)(4,4)(5,6)(6,5))La niña vió un libro rojo
A girl saw a red book((1,1)(2,2)(3,3)(4,4)(5,6)(6,5))Una niña vió un libro rojo
I saw the red book((1,1)(2,2)(3,3)(4,5)(5,4))Yo vi el libro rojo
I saw a red book.
((1,1)(2,2)(3,3)(4,5)(5,4)) Yo vi un libro rojo
Feature: definitenessValues: definite, indefiniteFunction-of-*: subj, objMarked-on-head-of-*: noMarked-on-dependent: yesMarked-on-governor: noMarked-on-other: noAdd/delete-word: noChange-in-alignment: no
Feature Detection: Chinese
A girl saw a red book.
((1,2)(2,2)(3,3)(3,4)(4,5)(5,6)(5,7)(6,8))
有 一个 女人 看见 了 一本 红色 的 书 。
The girl saw a red book.
((1,1)(2,1)(3,3)(3,4)(4,5)(5,6)(6,7))
女人 看见 了 一本 红色的 书
Feature: definiteness
Values: definite, indefinite
Function-of-*: subject
Marked-on-head-of-*: no
Marked-on-dependent: no
Marked-on-governor: no
Add/delete-word: yes
Change-in-alignment: no
Feature Detection: Chinese
I saw the red book((1, 3)(2, 4)(2, 5)(4, 1)(5, 2))
红色的 书， 我 看见 了
I saw a red book.((1,1)(2,2)(2,3)(2, 4)(4,5)(5,6))我 看见 了 一本 红色的 书 。
Feature: definitenesValues: definite, indefiniteFunction-of-*: objectMarked-on-head-of-*: noMarked-on-dependent: noMarked-on-governor: noAdd/delete-word: yesChange-in-alignment: yes
Feature Detection: Hebrew
A girl saw a red book.((2,1) (3,2)(5,4)(6,3))
ראתה ספר אדוםילדה
The girl saw a red book((1,1)(2,1)(3,2)(5,4)(6,3))
ראתה ספר אדוםהילדה
I saw a red book.((2,1)(4,3)(5,2))
אדוםספרראיתי
I saw the red book.((2,1)(3,3)(3,4)(4,4)(5,3))
האדוםהספרראיתי את
Feature: definitenessValues: definite, indefiniteFunction-of-*: subj, objMarked-on-head-of-*: yesMarked-on-dependent: yesMarked-on-governor: noAdd-word: noChange-in-alignment: no
Feature Detection Feeds into…
• Corpus Navigation: which minimal pairs to pursue next.– Don’t pursue gender in Mapudungun– Do pursue definiteness in Hebrew
• Morphology Learning:– Morphological learner identifies the forms of the morphemes– Feature detection identifies the functions
• Rule learning:– Rule learner will have to learn a constraint for each morpho-
syntactic marker that is discovered• E.g., Adjectives and nouns agree in gender, number, and definiteness
in Hebrew.