AMTEXT: Extractionbased MT for Arabic

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AMTEXTExtraction-based MT for Arabic

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

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Background and Objectives

• Full MT of text is problematic
• Requires large amounts of resources, long
  development time
• Quality of output varies
• 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
• Text Extraction technology has made much progress
  in past decade TIPSTER, TREC, EELD
• Research Question Can Extraction-based MT result
  in improved accuracy and utility of information
  for analysts?

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Extraction-based MT

• Traditional Approach
• Develop information extraction capability for the
  source language
• Runtime Extractor produces a template of
  extracted feature-value information
• If desired, English Generator can render the
  information in the form of text
• Drawback Adapting extraction technology to a new
  foreign language is difficult
• Requires significant expertise in the foreign
  language
• Significant amounts of human development time
• Not clear that it is an attractive solution

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AMTEXT Approach

• Attempt to leverage from our work on automatic
  learning of MT transfer rules
• Develop an elicitation corpus specifically
  designed for targeted extraction patterns
• Learn generalized transfer rules for targeted
  extraction patterns from elicitation corpus
• Acquire high accuracy Named-Entity translation
  lexicon limited translation lexicon for
  targeted vocabulary
• Runtime use partial parser transfer rules to
  translate only the matched portions of SL text

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

• Different notion of rule generalization than in
  our full XFER approach
• Generalize from examples to NEs that play
  specific roles in target extraction pattern
• Verbs and function words may not be generalized
• Example

Sharon will meet with Bush today sharon yipagesh
im bush hayom
Goal Rule
SS NE-P yipagesh im NE-P TE -gt NE-P will
meet with NE-P TE((X1Y1) (X4Y5) (X5Y6))
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Acquisition of Named Entity Translation Lexicon

• Utilize Fei Huangs work on building Named Entity
  Translation Lexicons based on transliteration
  models
• NE Lexicon will be split into meaningful
  sub-categories PNs, Organizations, Locations,
  etc.
• NE translation lexicon augmented with NEs from
  elicited data
• Goal High coverage and high accuracy
  identification of NEs that play a part in the
  transfer rules

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Named Entity Translation Lexicon

• English-Arabic lexicon from Fei
• Trained on TIDES Newswire Data
• 7522 entries sorted by transliteration score
• Example

4.51948528108464 XXX Israel
AsrAAyl 4.05498190544419 XXX Kabul
kAbwl 3.66368346525326 XXX Paris
bArys 3.65527347080481 XXX Afghanistan
AfgAnstAn 3.47030997281853 XXX Pakistan
bAkstAn 3.23199522148251 XXX Moscow
mwskw 3.20392400497002 XXX Arafat
ErfAt 3.13060360328543 XXX Beirut
byrwt 3.06872591580516 XXX Russia rwsyA
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Named Entity Identification

• NE Identifinder for English
• Available from BBN
• Will be used for identifying English NEs within
  elicited data ? Arabic NEs from word alignments
• NE Identifinder for Arabic
• Requested from BBN, so far no response
• Will use if available, can manage without it
  (naïve identification based on NE translation
  lexicon)

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Acquisition of Limited Word Translation Lexicon

• Vocabulary of interest is limited based on
  specific actions and objects that are of interest
  ? scopeable on the English side
• Elicitation corpus serves as a high-quality
  initial source for extracting this translation
  lexicon
• Statistical word-to-word translation dictionary
  from SMT or EBMT can be used as a source for
  expanding coverage on the foreign language side
• Experiment if time/resources permit with
  incorporating expanded vocabulary into transfer
  rules

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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
• Handle expected high-levels of ambiguity

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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Scope of Pilot System

• Arabic-to-English
• Newswire text (available from TIDES)
• Limited set of actions (X meet Y) (X attend Y)
  (X hold Y) (X kill Y) (X announce Y)
• Limited translation patterns
• ltsubj-NEgt ltverbgt ltobjgt ltLOCgt ltTEgt
• Limited vocabulary

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Evaluation Plan

• Compare AMTEXT approach to full-text
  Arabic-to-English SMT, on a limited task of
  translation of relations within the scope of
  coverage
• Establish a test set for evaluation
• Define an appropriate metric Precision/Recall/F1
  of relations and entities
• Compare performance

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Current Status

• Initial small elicitation corpus translated and
  aligned
• Extraction of elicitation phrases from Penn-TB in
  advanced stages
• Identifying scope of coverage relations,
  actions, translation patterns
• Preliminary NE translation lexicon available

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Work Plan

• Creation of full elicitation corpus
  Nov-03
• Translation/align. of elicitation corpus
  Nov/Dec-03
• Install and integrate BBN English Identifinder
  Dec-03
• Acquire initial NE translation lexicon
  Dec-03
• Acquire initial word translation lexicon Dec-03
• Develop and integrate partial parser
  Dec-03/Feb-04
• Modify Transfer Engine for AMTEXT configuration
  Dec-03/Jan-04
• Integration of preliminary complete system
  Feb-04
• Design of evaluation Feb-04
• System testing and modifications Feb/Apr-04
• Test-set evaluation Apr-04