1. AMTEXT: Extraction-based MT for Arabic
Faculty:
Alon Lavie, Jaime Carbonell
Students and Staff:
Laura Kieras, Peter Jansen
Informant:
Loubna El Abadi

2. 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?
Nov 14, 2003
ITIC Site Visit

3. 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
Nov 14, 2003
ITIC Site Visit

4. 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
Nov 14, 2003
ITIC Site Visit

5. AMTEXT Extraction-based MT
Word-aligned elicited data
Source Text
Learning Module
Run Time Transfer System
Transfer Rules
Partial
Parser
S::S [NE-P pagash et NE-P TE] -> [NE-P met with NE-P TE] ((X1::Y1) (X4::Y4) (X5::Y5))
Extracted
Target Text
Transfer Engine
NE Translation Lexicon
Word Translation Lexicon
Nov 14, 2003
ITIC Site Visit

6. Elicitation Example
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7. Elicitation Example
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8. Elicitation Example
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9. Elicitation Example
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ITIC Site Visit

10. 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:
S::S [NE-P yipagesh &im NE-P TE] -> [NE-P will meet with NE-P TE] ((X1::Y1) (X4::Y5) (X5::Y6))
Nov 14, 2003
ITIC Site Visit

11. Acquisition of Named Entity Translation Lexicon
Utilize Fei Huang’s 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
Nov 14, 2003
ITIC Site Visit

12. Named Entity Translation Lexicon
English-Arabic lexicon from Fei:
Trained on TIDES Newswire Data
7522 entries sorted by transliteration score
Example:
# XXX # # Israel # AsrAAyl
# XXX # # Kabul # kAbwl
# XXX # # Paris # bArys
# XXX # # Afghanistan # AfgAnstAn
# XXX # # Pakistan # bAkstAn
# XXX # # Moscow # mwskw
# XXX # # Arafat # ErfAt
# XXX # # Beirut # byrwt
# XXX # # Russia # rwsyA
Nov 14, 2003
ITIC Site Visit

13. 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)
Nov 14, 2003
ITIC Site Visit

14. 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
Nov 14, 2003
ITIC Site Visit

15. 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
Nov 14, 2003
ITIC Site Visit

16. 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:
<subj-NE> <verb> <obj> <LOC>* <TE>*
Limited vocabulary
Nov 14, 2003
ITIC Site Visit

17. 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
Nov 14, 2003
ITIC Site Visit

18. 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
Nov 14, 2003
ITIC Site Visit

19. 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
Nov 14, 2003
ITIC Site Visit