Data Elicitation for AVENUE

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Data Elicitation for AVENUE

• By Alison Alvarez
• Lori Levin
• Bob Frederking
• Jeff Good (MPI Leipzig)
• Erik Peterson

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Avenue System Diagram
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Goals for Corpus Creation and Elicitation

• Parallel corpus with high quality word alignment
• For a language with little or no digitized
  language resources
• Use a bilingual informant with no linguistic
  expertise

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Outline

• Elicitation
• Feature Detection
• The Functional-Typological Corpus
• Corpus Creation and Elicitation
• Corpus Navigation

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The Elicitation Tool
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Input to the Elicitation Tool

• Eliciting from English
• 1,2,3 Sg,pl person pronouns
• newpair
• srcsent I sing
• context
• comment
• newpair
• srcsent I sang
• context
• comment
• newpair
• srcsent I am singing
• context
• comment
• newpair
• srcsent You sang

• Eliciting from Spanish
• 1,2,3 Sg,pl person pronouns
• newpair
• srcsent Canto
• context
• comment
• newpair
• srcsent Canté
• context
• comment
• newpair
• srcsent Estoy cantando
• context
• comment
• newpair
• srcsent Cantaste

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Output of the elicitation process

• newpair
• srcsent Tú caíste
• tgtsent eymi ütrünagimi
• aligned ((1,1),(2,2))
• context tú Juan masculino, 2a persona del
  singular
• comment You (John) fell
• newpair
• srcsent Tú estás cayendo
• tgtsent eymi petu ütünagimi
• aligned ((1,1),(2 3,2 3))
• context tú Juan masculino, 2a persona del
  singular
• comment You (John) are falling
• newpair
• srcsent Tú caíste
• tgtsent eymi, ütrunagimi
• aligned ((1,1),(2,2))
• context tú María femenino, 2a persona del
  singular

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Elicitation Corpus

• Elicitation Corpus refers to the list of
  sentences in the major language.
• Not yet translated or aligned
• Field workers call it a questionnaire.

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Feature Detection

• Identify meaning components that have
  morpho-syntactic consequences in the language
  that is being elicited.
• The gender of the subject is marked on the verb
  in Hebrew.
• The gender of the subject has no morpho-syntactic
  realization in Mapudungun.

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Feature detection feeds into

• Corpus Navigation which minimal pairs to pursue
  next.
• Dont pursue gender in Mapudungun
• Do pursue definiteness in Hebrew
• Morphology Learning
• Morphological rule learner identifies the forms
  of the morphemes
• Feature detection identifies the functions
• Rule learning
• Rule learner will have to learn a constraints
  corresponding to fact records.
• E.g., Adjectives and nouns agree in gender,
  number, and definiteness in Hebrew.

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Other uses of Feature Detection

• A human-readable reference grammar can be
  generated from fact records.
• A human analyst knows Northern Ostyak, and then
  has to translate a document in Eastern Ostyak.
  The only reference grammar of Eastern Ostyak is
  written in Hungarian, which the analyst does not
  speak. An Eastern Ostyak consultant who speaks
  Russian translates the Elicitation Corpus from
  Russian to Eastern Ostyak. The analyst learns
  about Eastern Ostyak from the automatically
  generated fact records.

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Other uses of Feature Detection

• A human-readable reference grammar can be
  generated from fact records.
• A human analyst knows Northern Ostyak, and then
  has to translate a document in Eastern Ostyak.
  The only reference grammar of Eastern Ostyak is
  written in Hungarian, which the analyst does not
  speak. An Eastern Ostyak consultant who speaks
  Russian translates the Elicitation Corpus from
  Russian to Eastern Ostyak. The analyst learns
  about Eastern Ostyak from the automatically
  generated fact records.
• Im not really sure whether the only grammar of
  Eastern Ostyak is written in Hungarian. There is
  one reference grammar of Northern Ostyak written
  in English. All other Ostyak materials are in
  Hungarian, Russian, and German.
• The Ostyaks are subsistence hunters, and Eastern
  Ostyak is nearly extinct, so there is no real
  need for government translators.
• Other Siberian and Central Asian languages with
  similar scarcity of resources may be important.

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Other uses of Feature Detection

• Help a field worker
• Instead of Elicit by day analyze by night (in
  order to know what to elicit the next day), go to
  sleep and look at the fact records in the
  morning.
• We have been working with people at EMELD and MPI
  Leipzig.

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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 definiteness
• Values definite, indefinite
• Function-of- subj, obj
• Marked-on-head-of- no
• Marked-on-dependent yes
• Marked-on-governor no
• Marked-on-other no
• Add/delete-word no
• Change-in-alignment no

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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))
• ? ?? ?? ?? ? ?? ?? ? ? ?
• The girl saw a red book.
• ((1,1)(2,1)(3,3)(3,4)(4,5)(5,6)(6,7))
• ?? ?? ? ?? ??? ?

• 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

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Feature Detection Chinese

• I saw the red book
• ((1, 3)(2, 4)(2, 5)(4, 1)(5, 2))
• ??? ?, ? ?? ?
• I saw a red book.
• ((1,1)(2,2)(2,3)(2, 4)(4,5)(5,6))
• ? ?? ? ?? ??? ? ?

• Feature definitenes
• Values definite, indefinite
• Function-of- object
• Marked-on-head-of- no
• Marked-on-dependent no
• Marked-on-governor no
• Add/delete-word yes
• Change-in-alignment yes

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Feature Detection Hebrew

• A girl saw a red book.
• ((2,1) (3,2)(5,4)(6,3))
• ???? ???? ??? ????
• The girl saw a red book
• ((1,1)(2,1)(3,2)(5,4)(6,3))
• ????? ???? ??? ????
• I saw a red book.
• ((2,1)(4,3)(5,2))
• ????? ??? ????
• I saw the red book.
• ((2,1)(3,3)(3,4)(4,4)(5,3))
• ????? ?? ???? ?????

• Feature definiteness
• Values definite, indefinite
• Function-of- subj, obj
• Marked-on-head-of- yes
• Marked-on-dependent yes
• Marked-on-governor no
• Add-word no
• Change-in-alignment no

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AVENUE Elicitation Corpora

• The Functional-Typological Corpus
• Based on microtheories of meanings that may have
  morpho-syntactic realization
• The Structural Elicitation Corpus
• Based on sentence structures from the Penn
  TreeBank

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The Functional Typological Corpus

• lt/featuregt
• ltfeaturegt
• ltfeature-namegtc-my-polaritylt/feature-namegt
• ltvaluegt
• ltvalue-namegtpolarity-positivelt/value-namegt
• lt/valuegt
• ltvaluegt
• ltvalue-namegtpolarity-negativelt/value-namegt
• lt/valuegt
• ltnotegtStick to the two obvious values of polarity
  for now.lt/notegt
• lt/featuregt

• Feature Name c-my-polarity
• Values positive, negative
• Note Stick to the two obvious values of polarity
  for now.

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Functional Typological Corpus

• In XML
• XSLT scripts can format it into human-readable
  text or into data structures.
• Currently contains around 50 features and a few
  hundred values.
• Still under development.

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Functional Typological Corpus Representation of
Who is at the meeting

• ((subj ((np-my-general-type pronoun-type)(np-my-p
  erson person-unk)
• (np-my-number num-sg)(np-my-animacy anim-human)
• (np-my-function fn-predicatee)
• (np-d-my-distance-from-speaker distance-neutral)
• (np-my-emphasis emph-no-emph)
• (np-my-info-function info-neutral)
• (np-pronoun-exclusivity exclusivity-n/a)
• (np-pronoun-antecedent-function antecedent-n/a)
• (np-pronoun-reflexivity reflexivity-n/a)))
• (predicate ((loc-roles loc-general-at)))
• Continued on next slide

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Continued Who is at the meeting

• (c-my-copula-type locative)(c-my-secondary-type
  secondary-copula) (c-my-polarity
  polarity-positive) (c-my-function
  fn-main-clause)(c-my-general-type
  open-question)(gap-function gap-copula-subject)(c-
  my-sp-act sp-act-request-information)(c-v-my-gramm
  atical-aspect gram-aspect-neutral)(c-v-my-absolute
  -tense present) (c-v-my-phase-aspect
  durative)(c-my-headedness-rc rc-head-n/a)(c-my-min
  or-type minor-n/a)(c-my-restrictivess-rc
  rc-restrictive-n/a)(c-my-answer-type
  ans-n/a)(c-my-imperative-degree
  imp-degree-n/a)(c-my-actor's-status
  actor-neutral)(c-my-focus-rc focus-n/a)(c-my-gaps-
  function gap-n/a)(c-my-relative-tense
  relative-n/a)(c-my-ynq-type ynq-n/a)(c-my-actor's-
  sem-role actor-sem-role-neutral)(c-v-my-lexical-as
  pect state))

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Why is the corpus represented as a set of feature
structures?

• Multiple elicitation languages
• Generate the English and Spanish elicitation
  corpora from the same internal representation
• Easy to add a new elicitation language
• Write a GenKit grammar to generate sentences from
  the same internal representation

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Why is the corpus represented as a set of feature
structures?

• Feature structure represents things that are not
  expressed in the major language
• These things show up as comments in the
  elicitation corpus
• I am singing (comment female)
• May eventually use pictures and discourse context
• We actually want to elicit the meaning associated
  with the feature structure. English and Spanish
  are just vehicles for getting at the meaning.

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Corpus Creation Tools

• The elicitation corpus can be changed and new
  corpora can be created.

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Motivation for Corpus Creation Tools

• Make new corpora easily
• Add a new tense (e.g., remote past) and
  automatically get all the combinations with other
  features
• Make a specialized corpus for a limited semantic
  domain or a specific language family

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Motivation for Corpus Creation Tools

• Combinatorics
• For example, all combinations of person, number,
  gender, tense, etc.
• Too much bookkeeping for a human corpus creator,
  and too time consuming

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Where do the feature structures come from?

• A linguist formulates a Multiply
• The multiply specifies a set of feature structures

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A Multiply

• ((subj ((np-my-general-type pronoun-type
  common-noun-type)
• (np-my-person person-first person-second
  person-third)
• (np-my-number num-sg num-pl)
  
• (np-my-biological-gender bio-gender-male
  bio-gender-female) (np-my-function
  fn-predicatee)))
• (predicate ((np-my-general-type
  common-noun-type)
• (np-my-definiteness definiteness-minus)
  (np-my-person person-third)
• (np-my-function predicate))) (c-my-copula-type
  role)
• (predicate ((adj-my-general-type
  quality-type))) (c-my-copula-type attributive)
  
• (predicate ((np-my-general-type
  common-noun-type)
• (np-my-person person-third) (np-my-definiteness
  definiteness-plus)
• (np-my-function predicate))) (c-my-copula-type
  identity)
• (c-my-secondary-type secondary-copula)
  (c-my-polarity all)
• (c-my-function fn-main-clause)(c-my-general-type
  declarative)
• (c-my-speech-act sp-act-state) (c-v-my-grammatical
  -aspect gram-aspect-neutral)
• (c-v-my-lexical-aspect state) (c-v-my-absolute-ten
  se past present future)
• (c-v-my-phase-aspect durative))
• This multiply expands to 288 feature structures.

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There is a GUI for making Multiplies

• Demo available on request

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GenKit Grammar

• Use GenKit for generation
• declarative
• (ltsgt gt (ltnpgt ltvpgt ltnpgt ltscgt)
• (((x0 c-my-general-type) c declarative)
• ((x2 verb-form) fin)
• ((x3 c-my-copula-type) (x0
  c-my-copula-type))
• ((x4 d-speaker-gender) (x0
  d-speaker-gender))
• ((x4 d-hearer-gender) (x0
  d-hearer-gender))
• ((x4 d-my-formality) (x0
  d-my-formality))
• ((x3 np-my-number) (x0 np-my-number))
• ((x3 np-my-animacy) (x0
  np-my-animacy))
• ((x3 np-my-biological-gender) (x0
  np-my-biological-gender))
• (x3 (x0 predicate))
• (x1 (x0 subj))
• (x2 x0)))

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GenKit Lexicon

• Pronouns
• (word ((cat n) (root you) (pred pro)
  (np-my-person person-second)
• (np-my-animacy anim-human)
  (np-my-general-type pronoun-type)))
• (word ((cat n) (root I) (pred pro)
  (np-my-person person-first) (np-my-number num-sg)
  
• (np-my-animacy anim-human)
  (np-my-general-type pronoun-type)))
• (word ((cat n) (root we) (pred pro)
  (np-my-person person-first) (np-my-number num-pl)
  
• (np-my-animacy anim-human)
  (np-my-general-type pronoun-type)))
• (word ((cat n) (root we) (pred pro)
  (np-my-person person-first)
• (np-my-number num-dual) (np-my-animacy
  anim-human)
• (np-my-general-type pronoun-type)))
• (word ((cat n) (root she) (pred pro)
  (np-my-person person-third)
• (np-my-number num-sg) (np-my-biological-g
  ender bio-gender-female)
• (np-my-animacy anim-human)
  (np-my-general-type pronoun-type)))

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Comments are also generated

• I one female sang
• Use comments for things that are not expressed in
  English.

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Convert to Elicitation Format(input to
Elicitation Tool)

• original WHO IS AT THE BOX
• full comment
• Sentence WHO IS AT THE BOX
• original I ONE-WOMAN AM PN_FEMALE ONE-WOMAN
  
• full comment NP1 ONE-WOMAN
• Sentence I AM PN_FEMALE
• original WILL I ONE-WOMAN BE THE TEACHER
• full comment NP1 ONE-WOMAN
• Sentence WILL I BE THE TEACHER

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Eight Basic Steps for Corpus Creation

1. Write FVD and format into data structure
2. Gather Exclusions (restrictions on co-occurrence
   of features
3. Design the Multiply
4. Get a full set of Feature Structures
5. Design Grammar and Comments
6. Design Lexicon
7. Generate Sentences from Feature Structures
8. Convert to Elicitation Format

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Can make other types of corpora

• The Elicitation Corpus does not have to be
  functional-typological

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Alternative Corpora The Medical Corpus
((subj ((body-parts all) (Poss
((np-my-general-type pronoun-type)
(np-my-person all)
(np-my-number num-sg num-pl)
(np-my-animacy anim-human)
(np-my-use possessive))) (Pred ((symptoms
all)) (c-my-general-type declarative) (c-my-spee
ch-act sp-act-state) (c-v-my-grammatical-aspect
gram-aspect-neutral) (c-v-my-lexical-aspect
state) (c-v-my-absolute-tense present))

• Feature Body-PartsValues     
• part-hand   Restrictions
• part-finger  Restrictions
• part-tooth   Restrictions symptom_redness
• symptom_scratch symptom_numbness
• symptom_cut symptom_lump
• symptom_rash
• symptom_puncture
• symptom_bruise
• symptom_frozen
• part-eye    Restrictions symptom_rash
• part-arm    Restrictions

The Result YOUR ARM IS RED YOUR ARM IS
SCRATCHED YOUR ARM IS NUMB YOUR ARM IS
NIL YOUR ARM HAS A/N INFECTION
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Corpus Navigation

• While the Elicitation Corpus for any one target
  language (TL) can be kept to a reasonable size,
  the universal Elicitation Corpus must check for
  all phenomena that might occur in any langauge.
• Since the universal corpus cannot be kept to a
  reasonable size, Corpus Navigation is necessary.
  
• Facts discovered about a particular TL early in
  the process constrain what needs to be looked for
  later in the process for that TL. Thus this is a
  dynamic process, different for each TL.

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Corpus Navigation search

• Search process, with the informant in the inner
  loop, expanding search states he/she is given as
  SL sentences by supplying the corresponding TL
  sentence and alignments.
• Analogously to game search, there is an "opening
  book" of moves (SL sentences to check for all
  languages), until enough inforrmation has been
  gathered to make intelligent search choices.
• The hueristic function driving the search process
  is Relative Info Gain
• RIG(YX) H(Y) - H(YX)/H(Y)
• The system reduces the remaining entropy in its
  knowledge of the language as much as possible.
• There should also be a cost factor, estimating
  the human effort required to expand the node.
• To make the process efficient enough, we will
  create "decision graphs", similar to RETE
  networks, that cache information so only the
  information that changes needs to be recomputed.