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Flow Network Models for Sub-Sentential Alignment . Ying Zhang (Joy) Advisor: Ralf Brown Dec 18 th , 2001. Background. Sub-sentential alignment problem Given a bilingual parallel sentence pair, to find the correspondence between source words and target words

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flow network models for sub sentential alignment

Flow Network Models for Sub-Sentential Alignment

Ying Zhang (Joy)

Advisor: Ralf Brown

Dec 18th, 2001

background
Background
  • Sub-sentential alignment problem
    • Given a bilingual parallel sentence pair, to find the correspondence between source words and target words
    • One of the major issues in Data-Driven MT (SMT/EBMT)
  • Some approaches
    • IBM Model 1
    • Smooth Injective Map Recognizer [I. Dan Melamed]

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

flow network
Flow Network
  • A well-studied area since 1950’s
  • Used widely in electrical engineering, computer science, social science and economic problems
  • Any system involving a binary relation can be represented by a network

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

flow network4
Flow Network

[Jensen]

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

minimum cost flow
Minimum Cost Flow
  • One of the basic problems in flow network theory
  • For a network G= (V,E),

Total cost =

  • Minimum cost problem: given a network, assign the flow for each arc, so that the total cost of the network is minimum

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

alignment as a flow network
Alignment as a Flow Network

The “abstract concepts” are transformed through this network

Flow>=1, if there is alignment between two words,

Flow =0. O/W

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

alignment as a mini cost flow network
Alignment as a Mini-cost Flow Network
  • Assume the alignment probabilities only lies in the possibilities of words across languages. (Alignments between other words do not have impact on this pair), then
  • For word pair (si,tj), assign the cost for the arc as -lnp(si,tj), then the mini-cost flow network corresponds to the maximum P(a,s,t)

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

pure mini cost flow algorithm
Pure Mini-Cost Flow Algorithm
  • Primal simplex algorithm

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

pure mini cost flow algorithm cont
Pure Mini-cost Flow Algorithm (Cont.)

[Jensen]

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

pure mini cost flow algorithm cont10
Pure Mini-cost Flow Algorithm (Cont.)
  • Two phase algorithm

[Jensen]

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

our model
Our Model

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

solve the network phase 1
Solve the Network – Phase 1
  • Phase 1: to get the basis solution (solved on paper)

BridgeArc

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

solve the network phase 2
Solve the Network – Phase 2
  • If not optimal:
    • Using bridgeArc to calculate the dual values of tgt nodes
    • If the minimal dual values of arcs in n_0 < 0

Then make this arc to be the new bridge arc;

Insert the previous bridgeArc to n_0;

    • Else If all dual values in n_0 >0

If min(dual values of n_0) < max(dual values of n_1)

make this n_1 arc to be the new bridge arc;

insert the previous bridgeArc to n_1;

Else optimal, stop

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

update the cost by e m
Update the Cost by E.M.
  • Each sentence pair is represented by a network
  • After the network is solved, update the counts and probability contribution of the word pairs in the solved network
  • Update the probability of word association between source and target language
  • Using Good-Turing smoothing
  • Does not work very well so far! –High frequency words

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

model training
Model Training
  • Sentence level aligned bilingual corpus (hknews)
  • Stemmed by Porter stemmer
  • Building a seed statistical dictionary using Ralf’s method [Brown97]
  • Combined with the Chinese-English glossary to get a seed dictionary (coverage != 100%)
  • Solve the network and update the probability until the total cost of the corpus converge

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

performance results
Performance & Results
  • Fast (align 10,000 sentence pairs in 2 minutes*)
      • * words in sentence <=25
      • * system ran on PC with 128 RAM
  • Using only the seed dictionary, tested on 30 sentence pairs:
    • Recall: 53%~61%
      • (because of the coverage of the seed dictionary)
    • Precision: 74%~76%
    • Expected to achieve a higher recall when E.M. works properly

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

future work
Future Work
  • Currently, the model is no more powerful than IBM model1
  • We are planning to integrate the monolingual co-occurrence probability and the bilingual association probability into the model

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

references
References
  • Éric Gaussier, “Flow Network Models for Word Alignment and Terminology Extraction from Bilingual Corpora”, Proceedings of the 36th Annual Meeting of the association for Computational Linguistics and the 17th International Conference on Computational Linguistics, COLING-ACL'98. Montreal, Canada
  • Paul A. Jensen and Jonathan F. Bard, “Operations Research Models and Methods”, http://www.me.utexas.edu/~jensen/ORMM/index.html
  • Ralf D. Brown, "Automated Dictionary Extraction for ``Knowledge-Free'' Example-Based Translation". In Proceedings of the Seventh International Conference on Theoretical and Methodological Issues in Machine Translation, p. 111-118. Santa Fe, July 23-25, 1997.

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

acknowledgement
Acknowledgement
  • Many thanks to Jian Zhang, Katharina Probst, and Alicia Tribble for their help !

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University

questions and comments
Questions and Comments?

Language Technologies Institute

School of Computer Sciences

Carnegie Mellon University