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Confidence Estimation for Machine Translation

Confidence Estimation for Machine Translation. J. Blatz et.al, Coling 04 SSLI MTRG 11/17/2004 Takahiro Shinozaki. Abstract. Detailed study of CE for machine translation Various machine learning methods CE for sentences and for words Different definitions of correctness Experiments

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Confidence Estimation for Machine Translation

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  1. Confidence Estimation for Machine Translation J. Blatz et.al, Coling 04 SSLI MTRG 11/17/2004 Takahiro Shinozaki

  2. Abstract • Detailed study of CE for machine translation • Various machine learning methods • CE for sentences and for words • Different definitions of correctness • Experiments • NIST 2003 Chinese-to-English MT evaluation

  3. 1 Introduction • CE can improve usability of NLP based systems • CE techniques is not well studied in Machine translation • Investigate sentence and word level CE

  4. CE Score CE Score Threshold Threshold Binary output Binary output 2 Background Strong vs. weak CE Strong CE: require probability Correctness probabilities Weak CE: require only binary classification NOT necessary probability

  5. 2 Background Has CE layer or not No distinct CE layer NLP system Has distinct CE Layer NLP system • Require a training corpus • Powerful and modular CE module Naïve Bayes, NN, SVM etc…

  6. 3 Experimental Setting Correct or Not Input sentences N-best C Hyp Src Train Translation system ISI Alignment Template MT system Validation Test Reference sentences

  7. 3.1 Corpora • Chinese-to-English • Evaluation sets from NIST MT competitions • Multi reference corpus from LDC

  8. 3.2 CE Techniques • Data : A collection of pairs (x,c) • X: feature vector, c: correctness • Weak CE • X  score • X  MLP  score (Regressing MT evaluation score) • Strong CE • X  naïve Bayes  P(c=1|x) • X  MLP  P(c=1|x)

  9. C x1 x2 xD 3.2 Naïve Bayes (NB) • Assume features are statistically independent • Apply absolute discounting

  10. 3.2 Multi Layer Perceptron • Non-linear mapping of input features • Linear transformation layers • Non-linear transfer functions • Parameter estimation • Weak CE (Regression) • Target: MT evaluation score • Minimizing a squared error loss • Strong CE (Classification) • Target: Binary correct/incorrect class • Minimizing negative log likelihood

  11. 3.3 Metrics for Evaluation • Strong CE metric:Evaluates probability distribution • Normalized cross entropy (NCE) • Weak CE metrics:Evaluates discriminability • Classification error rate (CER) • Receiver operating characteristic (ROC)

  12. 3.3 Normalized Cross Entropy • Cross Entropy (negative log-likelihood) Estimated probability from CE module • Normalized Cross Entropy (NCE) Empirical probability obtained from test set

  13. 3.3 Classification Error Rate • CER: Ratio of samples with wrong binary (Correct/Incorrect) prediction • Threshold optimization • Sentence-level experiments: test set • Word-level experiments: validation set • Baseline

  14. 3.3 Receiver operating characteristic Prediction 1 ROC curve Better Correct-accept-ratio Fact IROC random 0,0 1 Correct-reject-ratio Cf.

  15. 4 Sentence Level Experiments • MT evaluation measures • WERg: normalized word error rate • NIST: sentence-level NIST score • “Correctness” definition • Thresholding WERg • Thresholding NIST • Threshold value • 5% “correct” examples • 30% “correct” examples

  16. 4.1 Features • Total of 91 sentence level features • Base-Model-Intrinsic • Output from 12 functions for Maximum entropy based base-system • Pruning statistics • N-best List • Rank, score ratio to the best, etc… • Source Sentence • Length, ngram frequency statistics, etc… • Target Sentence • LM scores, parenthesis matching, etc… • Source/Target Correspondence • IBM model1 probabilities, semantic similarity, etc…

  17. 4.2 MLP Experiments • MLPs are trained on all features for the four problem settings • Classification models are better than regression model • Performance is better than baseline N:NIST BASE CER W:WERg 3.21 32.5 5.65 32.5 Strong CE (Classification) Weak CE (Regression) N/A Table 2

  18. 4.3 Feature Comparison • Compare contributions of features • Individual feature • Group of features • All: All features • Base: base model scores • BD: base-model dependent • BI: base model independent • S: apply to source sentence • T: apply to target sentence • ST: apply to source and target sentence

  19. ALL Base BD BI S T ST 4.3 Feature Comparison (results) • Base All • BD > BI • T>ST>S • CE Layer > No CE Layer Exp. Condition: NIST 30% Table 3 Figure 1

  20. 5 Word Level Experiments • Definition of word correctnessA word is correct if: • Pos: occurs exactly at the same position as reference • WER: aligned to reference • PER: occurs in the reference • Select a “best” transcript from multiple references • Ratio of “correct” words • Pos(15%) < WER(43%) < PER(64%)

  21. 5.1 Features • Total of 17 features • SMT model based features (2) • Identity of alignment template, whether or not translated by a rule • IBM model 1 (1) • Averaged word translation probability • Word posterior and Related measures (3x3) • Target language based features (3+2) • Semantic features by WordNet • Syntax check, number of occurrences in the sentence WPP-any WPP-source WPP-target

  22. 5.2 Performance of Single Features • Experimental setting • Naïve Bayes classifier • PER based correctness • WPP-any give the best results • WPP-any>model1>WPP-source • Top3>any of the single features • No gain for ALL Table 4

  23. 5.3 Comparison of Different models • Naïve Bayes, MLPs with different number of hidden units • All features, PER based correctness • Naïve Bayes MLP0 • Naïve Bayes < MLP5 • MLP5 NLP10 NLP20 Figure 2

  24. 5.4 Comparison of Word Error Measures • Experimental settings • MLP20 • All features Table 5 • PER is the easiest to lean

  25. 6 Conclusion • Separate CE layer is useful • Features derived from base model are better than external ones • N-best based features are valuable • Target based features are more valuable than those not • MLPs with hidden units are better than naïve Bayes

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