Recent Trends in MT Evaluation: Linguistic Information and Machine Learning

1. Recent Trends in MT Evaluation: Linguistic Information and Machine Learning Jason Adams 11-734 2008-03-05 Instructors: Alon Lavie Stephan Vogel

2. Outline • Background • Machine Learning • Linguistic Information • Combined Approaches • Conclusions

3. Background • Fully automatic MT Eval is as hard as MT • If we could judge with certainty that a translation is correct, reverse the process and generate a correct translation • Reference translations help to close this gap

4. Background: Adequacy and Fluency • Adequacy • How much of the meaning in the source sentence that is preserved in the hypothesis • Reference translations are assumed to achieve this sufficiently • Fluency • How closely the hypothesis sentence conforms to the norms of the target language • Reference translations are a subset of target language

5. Background: Human Judgments • Judge on a scale for adequacy and fluency • Agreement between judges is low • Judgment scores normalized • Blatz et al (2003)

6. Background: Evaluating Metrics • Correlation with human assessments (judgments) • Pearson Correlation • Spearman Rank Correlation • Adding more references helps BLEU but hurts NIST (Finch et al. 2004)

7. Background: BLEU • Papineni et al. (2001) • First automatic MT metric to be widely adopted • Geometric mean of modified n-gram precision • Criticisms: • Poor sentence level correlation • Favors statistical systems • Ignores recall • Local word choice more important than global accuracy

8. Background: METEOR • Banerjee and Lavie (2005). • Addresses some of the shortcomings of BLEU • Uses recall of best reference • Attempts to align hypothesis and reference • Better correlation with human judgments • Optionally uses WordNet and Porter stemming

9. Outline • Background • Machine Learning • Linguistic Information • Combinations • Conclusions

10. Machine Learning: Kulesza & Shieber (2004) • Frame the MT Evaluation problem as a classification task • Can we predict if a sentence is generated by a human or a machine by comparing against reference translations?

11. Machine Learning: Kulesza & Shieber (2004) • Derived a set of features (partially based on BLEU) • Unmodified n-gram precisions (1 to 5) • Min and max ratio of hypothesis to reference length • Word error rate • minimum edit distance between hypothesis and any reference • Position-independent word error rate • shorter translation removed from longer and size of remaining set returned

12. Machine Learning: Kulesza & Shieber (2004) • Trained an SVM using classification • Positive: human translation • Negative: machine translation • Score is output of SVM • Distance to hyperplane is treated as a measure of confidence • Classification Accuracy • ~59% for human examples (positive) • ~70% for machine examples (negative)

13. Machine Learning: Kulesza & Shieber (2004) • Compared to BLEU, WER, PER, F-Measure at the sentence level

14. Outline • Background • Machine Learning • Linguistic Information • Combinations • Conclusions

15. Linguistic Information: Liu & Gildea (2005) • Introduce syntactic information • Use Collins parser on hypothesis and reference translations • Looked at three different metrics for comparing trees

16. Linguistic Information: Liu & Gildea (2005) • Subtree Metric (STM) • D – depth of trees considered • Count is # times subtree appears in any reference • Clipped count limits count to the maximum number of times it appears in any one reference

17. Linguistic Information: Liu & Gildea (2005) • Kernel-based Subtree Metric (TKM) • H(t) is a vector of counts for all subtrees of t • H(t1) · H(t2) counts subtrees in common • Use convolution kernels (Collins & Duffy, 2001) to compute in polynomial time • counting all subtrees would be exponential in the size of the trees

18. Linguistic Information: Liu & Gildea (2005) • Headword Chain Metric (HWCM) • Convert phrase-structure parse into dependency parse • Each mother-daughter relationship in the dependency parse is a headword chain of length 2 • No siblings included in any headword chain • Score computed in the same fashion as STM • Other two metrics have dependency versions

19. Linguistic Information: Liu & Gildea (2005) • Data is from MT03 and JHU Summer Workshop (2003) Correlation with overall judgments for one MT system (E15) Correlation with fluency judgments for one MT system (E15)

20. Linguistic Information: Liu & Gildea (2005) • Corpus level judgments for MT03

21. Linguistic Information: Pozar & Charniak (2006) • Propose the Bllip metric • Intuition: meaning-preserving transformations in sentences should not heavily impact dependency structure • Perhaps intuitive, but unsubstantiated

22. Linguistic Information: Pozar & Charniak (2006) • Parse hypothesis and reference translations with the Charniak parser • Construct dependency parses from the output parse trees • Given a lexical head pair (w1, w2) it is a dependency if: • w1 != w2 • w1 is the lexical head of a constituent immediately dominating the constituent of which w2 is the head

23. Linguistic Information: Pozar & Charniak (2006) • Construct all dependency pairs for the hypothesis and reference translation • If multiple reference translations, compare them one at a time • Compute precision and recall to score • Formula for doing so not explicitly stated, but probably F1

24. Linguistic Information: Pozar & Charniak (2006) • Evaluation was performed by comparing the biggest discrepancies between Bllip and BLEU and determining which was more accurate • Results suggest Bllip makes better choices than BLEU • Results aren’t directly given

25. Linguistic Information: Pozar & Charniak (2006) • Fairly weak paper • Evaluation is basically just “eye-balled” • But, simple headword bi-chains seem to perform as well as BLEU • Unfortunately, cannot be reliably compared

26. Linguistic Information: Owczarzak et al. (2007) • Extended work by Liu & Gildea (2005) • They used unlabeled dependency parses • Insight: having more information about grammatical relations might be helpful • X is the subject of Y • X is a determiner of Y

27. Linguistic Information: Owczarzak et al. (2007) • Used an LFG parser to generate f-structures that contain information about grammatical relations

28. Linguistic Information: Owczarzak et al. (2007) • Types of dependencies • Predicate only • Predicate-value pair, i.e. grammatical relations • Non-predicate • Tense • Passive • Adjectival degree (comparative, superlative) • Verb particle • Etc. • Extended HWCM from Liu & Gildea (2005) to use these labeled dependencies

29. Linguistic Information: Owczarzak et al. (2007) • How do you account for parser noise? • The positions of adjuncts should not affect f-structure in an LFG parse • Constructed re-orderings for 100 English sentences • Re-ordered sentence treated as translation hypothesis • Original sentence treated as reference translation

30. Linguistic Information: Owczarzak et al. (2007)

31. Linguistic Information: Owczarzak et al. (2007) • Solution: introduce n-best parses • Tradeoff with computation time • Used 10-best

32. Linguistic Information: Owczarzak et al. (2007) • Obtained precision and recall for each hypothesis, reference pair • Four examples for each machine hypothesis • Extended matching using WordNet synonyms • Extended with partial matches • One part of a grammatical relation matches and the other may or may not • Computed F1 • Tried different values for the weighted harmonic mean but saw no significant improvement * * Personal communication with Karolina Owczarzak

33. Linguistic Information: Owczarzak et al. (2007) • Evaluated using Pearson correlation with un-normalized human judgment scores • Values ranging from 1 to 5 • Their metric using 50-best parses and WordNet performed the best on fluency • METEOR with WordNet performed best on adequacy and overall • 50-best + partial matching performed slightly lower than METEOR overall • Significantly outperformed BLEU * Personal communication with Karolina Owczarzak

34. Outline • Background • Machine Learning • Linguistic Information • Combinations • Conclusions

35. Combinations: Albrecht & Hwa (2007) • Extended work by Kulesza & Shieber (2004) • Included work by Liu and Gildea with headword chains • Compared classification to regression using SVMs

36. Combinations: Albrecht & Hwa (2007) • Classification attempts to learn decision boundaries • Regression attempts to learn a continuous function • MT evaluation metrics are continuous • No clear boundary between “good” and “bad” • Instead of trying to classify as human or machine (Human-Likeness Classifier), try to learn the function of human judgments • Score hypothesis according to a rating scale

37. Combinations: Albrecht & Hwa (2007) • Features • Syntax based compared to reference • HWCM • STM • String-based metrics over large English corpus • Syntax-based metrics over a dependency treebank

38. Combinations: Albrecht & Hwa (2007) • Data was LDC Multiple Translation Chinese Part 4 • Spearman correlation instead of Pearson • Classification accuracy • Positively related but it’s possible to improve classification accuracy and not improve correlation • Human-Likeness classification seems inconsistent

39. Combinations: Albrecht & Hwa (2007) • It is possible to train using regression with reasonable size sets of training instances • Regression generalizes across data sets • Results showed highest correlation overall of metrics compared

40. Combinations: Albrecht & Hwa (2007)

41. Outline • Background • Machine Learning • Linguistic Information • Combinations • Conclusions

42. Conclusions • Evaluating the performance of MT evaluation metrics still has plenty of room for improvement • Given that humans don’t agree well on MT quality, correlation with human judgments is inherently limited

43. Conclusions • Machine learning • Only scratching the surface of possibilities • Finding the right way to frame the problem is not straightforward • Learning the function of how humans assess translations performs better than attempting to classify a translation as human or machine

44. Conclusions • Linguistic Information • Intuitively, this should be helpful • METEOR performs very well with limited linguistic information (synonymy) • Automatic parsers/NLP tools are noisy, so possibly compound the problem

45. Conclusions • Linguistic Information and Machine Learning • Combining the two leads to good results (Albrecht & Hwa 2007)

46. Conclusions • New directions • Machine learning with richer linguistic information • Labeled dependencies • Paraphrases • Are other machine learning algorithms better suited than SVMs? • Are there better ways of framing the evaluation question? • How well can these approaches be extended to task-specific evaluation?

47. Questions?

48. References • Joshua S. Albrecht and Rebecca Hwa. 2007. A re-examination of machine learning approaches for sentence-level MT evaluation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007). • Satanjeev Banerjee and Alon Lavie. 2005. Meteor: An automatic metric for MT evaluation with improved correlation with human judgments. In ACL 2005 Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, June. • John Blatz, Erin Fitzgerald, George Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis, and Nicola Ueffing. 2003. Confidence estimation for machine translation. Technical Report Natural Language Engineering Workshop Final Report, Johns Hopkins University. • Alex Kulesza and Stuart M. Shieber. 2004. A learning approach to improving sentence-level MT evaluation. In Proceedings of the 10th International Conference on Theoretical and Methodological Issues in Machine Translation (TMI), Baltimore, MD, October.

49. References • Ding Liu and Daniel Gildea. 2005. Syntactic features for evaluation of machine translation. In ACL 2005 Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, June. • Karolina Owczarzak, Josef van Genabith, and Andy Way. 2007. Labelled Dependencies in Machine Translation Evaluation. Proceedings of the ACL 2007 Workshop on Statistical Machine Translation: 104-111. Prague, Czech Republic. • Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, PA. • Michael Pozar and Eugene Charniak. 2006. Bllip: An Improved Evaluation Metric for Machine Translation. Master’s Thesis, Brown University.