1. Unsupervised Constraint Driven Learning for Transliteration Discovery M. Chang, D. Goldwasser, D. Roth, and Y. Tu

2. What I am going to do today… Goal 1 : Present the transliteration work  Get feedback! Goal 2: Think about this work with CCM  Tutorial ….  I will try to present this work in a slightly different way Some of them are my personal comment Different than our yesterday discussion  Please give us comment about this Make this work more general (not only transliteration)

3. Wait a sec! What is CCM?

4. Constraints Driven Learning Why Constraints?  The Goal: Building a good system easily  We have prior knowledge at our hand Why not inject knowledge directly ? How useful are constraints?  Useful for supervised learning [Yih and Roth 04] [many others]  Useful for semi-supervised learning [Chang et.al. ACL 2007]  Some times more efficient than labeling data directly

5. Unsupervised Constraint Driven Learning In this work  We do not use any label instance  Achieve to good performance that competitive several supervised model Compared to [Chang et.al. ACL 2007]  In ACL 07, they use a small amount of dataset (5-20) Reason: Bad Models can not benefit from constraints!  For some applications, we have very good resource We do not need labeled instances at all!

6. 6 In a nutshell: Traditional semi-supervised learning. Model can drift from the correct one. Model Unlabeled Data Prediction Label unlabeled data Feedback Learn from labeled data Unsupervised Learning Resource 

7. 7 In a nutshell: CODL Use constraints to generate better training samples in unsupervised learning. Prediction+ Constraints Model Unlabeled Data Prediction Feedback More accurate labeling Better Model CODL Improves “Simple” Model Using Expressive Constraints

8. Outline Constraint Driven Learning (CoDL) Transliteration Discovery Algorithm Experimental Results

9. Transliteration Generation (Not our focus) Given a Source Transliteration; What is the target transliteration?  Bush  布希  Sushi  壽司 Issues  Ambiguity : For the same source word, many different transliteration Think about Chinese  What we want: find the most widely used transliteration

10. Transliteration Discovery (Our focus) Problem Settings  Give you two list of words, map them! Advantages  A relatively easy problem  Can find the most widely used transliteration Assumption:  Source: English  Each source entities has a transliteration in the target candidates  Target candidates might not be named entities

11. Outline Constraint Driven Learning (CoDL) Transliteration Discovery Algorithm Experimental Results

12. Algorithm Outline Prediction Model How to use existing resource to construct the Model? Constraints? Learning Algorithm

13. The Prediction Model How do we make prediction?  Given a source word, how to predict the best target ? Model 1 : Vs, Vt  Yes or No  Issue: Not many obvious constraints can be added  Not a structure prediction problem Model 2: Vs, Vt  Hidden variables  Yes or No  Predicting F is a structure prediction algorithm  We can add constraints more easily

14. The Prediction Model Score for a pair A CCM formulation A slightly different scoring function More on this point in the next few slides Hidden Variables Violation

15. Prediction Model: Another View The scoring function looks like weight times features! If there is a bad feature, score  - ∞ Our Hidden variable (Feature Vectors):  Character Mapping

16. Everything (a,a), (o,O), (w,_),……

17. Algorithm Outline Prediction Model How to use existing resource to construct the Model? Constraints? Learning Algorithm

18. Resource: Romanization Table Hebrew, Russian  How can you type Hebrew or Russian? Use English Keyboard, C maps to A similar character “C” or “S” in Hebrew or Russian  Very easy to get  Ambiguous Special Case: Chinese (Pin Yin)  壽司  shòu s ī (Low ambiguity)  Map Pin-Yin to English (sushi)  Romanization Table? a  a

19. Initialize the Table Every character pair in the Romanization Table  Weight = 0  Everything else, -1  Could have better way to do initialization Note: All (v_s,v_t) will get zero without constraints

20. Algorithm Outline Prediction Model How to use existing resource to construct the Model? Constraints? Learning Algorithm

21. Constraints General Constraints  Coverage: all character need to be mapped at least once  No crossing: character mappings can not cross each other Language Specific Constraints  General Restricted Mapping  Initial Restricted Mapping  Length Restriction

22. Constraints Pin-Yin to English Many other works use similar information as well!

23. Algorithm Outline Prediction Model How to use existing resource to construct the Model? Constraints? Learning Algorithm

24. High-Level Overview Model  Resource  While Converge Use Model + Constraints to get Labels (for both F, y) Update Model with newly labeled F and y (without Constraints) (details in the next slide) Similar to ACL 07  Update the model without Constraints Difference from ACL 07  We get feedback from the labels of both hidden variables and output

25. Training Predict hidden variables and the labels Update Algorithm

26. Outline Constraint Driven Learning (CoDL) Transliteration Discovery Algorithm Experimental Results

27. Experimental Setting Evaluation  ACC: Top candidate is (one of) the right answer Learning Algorithm  Linear SVM with C = 0.5 Dataset  English-Hebrew 300: 300  English-Chinese 581:681  English-Russian 727:50648 (Target includes all words)

28. Results - Hebrew

29. Results - Russian

30. Analysis A small Russian subset was used here 1) Without Constraints (on features), Romanization Table is useless! 2) General Constraints are more important! 4) Better Constraints Lead to Better Final Results 3) Learning has great impact here! But constraints are very important, too!

31. Related Works (Need more work here) Learning the score for Edit Distance Previous transliteration works Machine translation?

32. Conclusion ML: unsupervised constraint driven algorithm  Use hidden variable to find more constraints (e.g. co-ref)  Use constraints to find “cleaner” feature representation Transliteration:  Usage of Normalization Table as the starting point We can get good results without training data  Right constraints (modeling) is the key Future Work  Transliteration Model: Better Model, Quicker Inference  CoDL: Other applications for unsupervised CoDL

33. 33 Constraint - Driven Learning (CODL) =learn(Tr) For N iterations do T=  For each x in unlabeled dataset y  Inference(x, ) T=T  {(x, y)} =  +(1-  )learn(T) Any supervised learning algorithm parametrized by Learn from new training data. Weight supervised and unsupervised model (Nigam2000*). Augmenting the training set (feedback). Any inference algorithm (with constraints). Inference(x,C, )

34. 34 Unsupervised Constraint - Driven Learning =Construct(Resource) For N iterations do T=  For each x in unlabeled dataset y  Inference(x, ) T=T  {(x, y)} =  +(1-  )learn(T) Construct the model with Resources Learn from new training data.  = 0 in this work Augmenting the training set (feedback). Any inference algorithm (with constraints). Inference(x,C, )