AMTEXT: Extractionbased MT for Arabic

1
AMTEXTExtraction-based MT for Arabic

• Faculty
• Alon Lavie, Jaime Carbonell
• Students and Staff
• Laura Kieras, Peter Jansen
• Informant
• Loubna El Abadi

2
Goals and Approach

• Analysts often are looking for limited concrete
  information within the text ? full MT may not be
  necessary
• Alternative rather than full MT followed by
  extraction, first extract and then translate only
  extracted information
• But how do we extract just the relevant parts
  in the source language?
• AMTEXT approach
• learn extraction patterns and their translations
  from small amounts of human translated and
  aligned data
• Combine with broad coverage Named-Entity
  translation lexicons
• System output translation of extracted
  information a structured representation

3
AMTEXT Extraction-based MT
Word-aligned elicited data
Source Text
Learning Module
Run Time Extract Transfer System
Transfer Rules
Filled Template
Partial Parser Transfer Engine
SS NE-P pagash et NE-P TE -gt NE-P met with
NE-P TE((X1Y1) (X4Y4) (X5Y5))
Extracted Target Text
Post-processor Extractor
NE Translation Lexicon
Word Translation Lexicon
4
Elicitation Example
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Learning Extraction Translation Patterns

• Elicited example
• Sharon nifgash hayom im bush
• Sharon met with Bush today
• After Generalization
• ltPERSONgt ltMEET-Vgt ltTEgt im ltPERSONgt
• ltPERSONgt ltMEET-Vgt with ltPERSONgt ltTEgt
• Resulting Learned Pattern Rule
• SS PERSON MEET-V TE im PERSON -gt PERSON
  MEET-V with PERSON TE
• ( (X1Y1)
• (X2Y2)
• (X3Y5)
• (X5Y4))

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Transfer Rule Formalism
SL the old man, TL ha-ish ha-zaqen NPNP
DET ADJ N -gt DET N DET ADJ ( (X1Y1) (X1Y3)
(X2Y4) (X3Y2) ((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)) )

• Type information
• Part-of-speech/constituent information
• Alignments
• x-side constraints
• y-side constraints
• xy-constraints,
• e.g. ((Y1 AGR) (X1 AGR))

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The Transfer Engine
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Partial Parsing

• Input Full text in the foreign language
• Output Translation of extracted/matched text
• Goal Extract by effectively matching transfer
  rules with the full text
• Identify/parse NEs and words in restricted
  vocabulary
• Identify transfer-rule (source-side) patterns
• Transfer Engine produces a complete lattice of
  transfer translations

Sharon, meluve b-sar ha-xuc shalom, yipagesh im
bush hayom
NE-P
NE-P
NE-P
TE
Sharon will meet with Bush today
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Post Processing

• Translation Selection Module
• select most complete and coherent translation
  from lattice based on scoring heuristics
• Structure Extraction
• Extract translated entities from the pattern and
  display in a structured table format
• Output Display
• Perl scripts construct HTML page for displaying
  complete translation results

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Translation Selection Module Features

• Goal Scoring function that can identify the most
  likely best match
• Lattice arc features from the transfer engine
• matched range of source
• matched parts of target
• transfer score
• partial parse

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Lattice Example

• Arafat to meet Peres in Brussels on Monday
• ErfAt yltqy byryz msAA AlAvnyn fy brwksl
• (1 1 "Arafat" 3 "ErfAt" "(PNAME,0 "Arafat")")
• (2 2 "will meet with" 3 "yltqy" "(MEET-V,5 "will
  meet with")")
• (3 3 "Peres" 3 "byryz" "(PNAME,1 "Peres")")
• (1 3 "Arafat will meet with Peres" 3 "ErfAt yltqy
  byryz" "((S,11 (PERSON,1 (PNAM
• E,0 "Arafat") ) (MEET-V,5 "will meet with")
  (PERSON,1 (PNAME,1 "Peres") ) ) )")
• (4 4 "msAA" 3 "msAA" "(UNK,0 "msAA")")
• (5 5 "Monday" 3 "AlAvnyn" "(DAY,0 "Monday")")
• (4 5 "on Monday" 2.9 "msAA AlAvnyn" "((TE,4
  (LITERAL "on")(DAY,0 "Monday") ) )")
• (1 5 "Arafat will meet with Peres on Monday" 3.2
  "ErfAt yltqy byryz msAA AlAvnyn
• " "((S,9 (PERSON,1 (PNAME,0 "Arafat") ) (MEET-V,5
  "will meet with") (PERSON,1 (P
• NAME,1 "Peres") ) (TE,4 (LITERAL "on")(DAY,0
  "Monday") ) ) )")
• (1 5 "Arafat will meet with Peres Monday" 3.1
  "ErfAt yltqy byryz msAA AlAvnyn" "
• ((S,9 (PERSON,1 (PNAME,0 "Arafat") ) (MEET-V,5
  "will meet with") (PERSON,1 (PNAM
• E,1 "Peres") ) (TE,5 (DAY,0 "Monday") ) ) )")
• (6 6 "fy" 3 "fy" "(UNK,2 "fy")")
• (7 7 "Brussels" 3 "brwksl" "(PLACE,0
  "Brussels")")

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Example Extracting Features

• 1 5 ? Length (tokens)
  of source segment (ar) (1)
• "Arafat will meet with Peres Monday" ? length
  of trans segment (2)
• 3.1
  ? transfer engine score (3)
• "ErfAt yltqy byryz msAA AlAvnyn" ? length of
  source segment (4)
• 1 2 3 4 5
• "((S,9 (PERSON,1 (PNAME,0 "Arafat") ) (MEET-V,5
  "will meet with") (PERSON,1 (PNAME,1 "Peres") )
  (TE,5 (DAY,0 "Monday") ) ) )"
• ? Transfer structure - full frame (S) or
  not? (5)
• Secondary feature (6) relative lengths of (2)
  over (4) the smaller, the more concise the
  source language match (less extraneous material,
  i.e. less chance of mistranslation).

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Selecting Best Translation
For each parse Pj in the lattice, calculate a
score Sj based on features fi with weight
coefficients wi, as follows
Weights wi trained by hill climbing
(training set / manual reference parse)
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Proof-of-Concept System

• Arabic-to-English
• Newswire text (available from TIDES)
• Very limited set of actions (X meet Y)
• Limited collection of translation patterns
• ltPerson-NEgt ltmeet-verbgt ltPerson-NEgt ltLOCgt ltTEgt
• Limited vocabulary and NE lexicon

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System Development

• Training corpus of 535 short sentences translated
  and aligned by bilingual informant
• 258 simple meeting sentences
• 120 Temporal Expressions
• 105 Location Expressions
• 52 Title Expressions
• Translation Lexicon of Names Entities (person
  names, organizations and locations) converted
  from Fei Huangs NE translation/transliteration
  work
• Pattern Generalizations semi-automatically
  learned from the training data
• Patterns manually enhanced with skipping
  markers
• Initial System integrated
• Development with informant on 74 sentence dev data

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Resulting System

• Transfer Grammar contains
• 21 transfer pattern rules
• 12 Meet Verb rules
• 4/17/11/17 Person/TE/LOC/PTitle high-level
  rules
• Transfer Lexicon contains 3070 entries (mostly
  names and locations)
• Estimated development effort/time
• 20 hours with informant
• 50 hours of lexical and rule development

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Evaluation

• Development set of 74 sentences
• Test set of 76 unseen sentences with meeting
  information
• Identified subset of each set on which meeting
  patterns could potentially apply (Good)
• 53 development sentences
• 44 test sentences

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Evaluation

• Translation-based
• Unigram token-based retrieval metrics precision
  / recall / F1
• Entity-based
• Recall for each role in the meeting frame (V, P1,
  P2, LOC and TE)
• Partial recall credit for partial matches
• Partial credit (50) for P1/P2 role interchange

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Evaluation Results
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Demonstration

• http//www-2.cs.cmu.edu/afs/cs/user/alavie/Avenue/
  tmp/demo20sep/met.dev.htm

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Conclusions

• Attractive methodology for joint extraction
  translation of Essential Elements of Information
  from full foreign language texts
• Rapid Development - circumvents need for
  developing high-quality full MT or high-quality
  IE technology for the foreign source language
• Effective use of bilingual informants
• Main Open Question Scalability
• Can this methodology be effective with much
  broader and more complex types of extracted EEIs?
• Is automatic learning of generalized patterns
  feasible and effective in such more complex
  scenarios?
• Can the selection heuristics effectively cope
  with the vast amounts of ambiguity expected in a
  large scale system?